SENTIMENT ANALYSIS OF RESPONSES FROM BENEFICIARIES TO LIVELIHOOD SUBPROJECTS IN ZAMBIA¶
Author: Nathan Namatama
Institution: Leibniz Institute of Ecological Urban and Regional Development and Technical University of Dresden
Year: 2025
Related Publication: The effects and impacts of livelihood activities and unplanned human settlement growth on greenspace and wetland landscapes in Zambia: A case of the three areas of the Pilot Programme for Climate Resilience (PPCR)
Purpose of the Analysis¶
The analysis is conducted on the data collection that was conducted in Zambia from 23rd July 2024 to 22nd September 2024. The primary data was collected using ArcGIS Survey123 application in an offline mode mostly in certain areas that did not have access to internet while those that ahd internet an online mode was used. The respondents were interviewed using a semi structured question and the responses were recorede in the application as they were responding to the questions.
The analysis is done in the framework of systems thinking of looking at deep leverage points in the governance for transformation of Social Ecologocal Systems so as to attain sustainable transfomation. The analysis is done in both qualitative and quantitatives (descriptions) to produce graphs and tables that are visualised within the jupyterlab notebook.
1. Importing Libraries¶
The liberarries that are needed for conducting the analysis are installed and downloaded. They are as follows:
- NLTK: For reading text and understanding it in a way that a human can do it
- Re: For text manipulation and pattern matching
- Pandas: For converting tables into a format understandable by the computer as well as visualisation
- Numpy: For conducting statistical culculations
- Matplotlib: For visualisation
- Seabron: For visualisation
- Io: For reading/writing binary and text data efficiently
- Csv: For reading the csv files
- Unicodeddata: For interacting with and analyzing Unicode characters
- String: For language analysis, user input, or file processing
- Plotly: For visualisation
- Plot_Likert: For visualisation of likert scales
- %matplotlib line: For visualisation within the jupyterlab notebook
- Nbconvert: For converting to HTML format
- WordCloud: For creating a word cloud
- Bigrams: For making words into pairs
- Trigrams: For making three words sequencies
In [1]:
import nltk
import re
from collections import Counter
from nltk.probability import FreqDist
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
from nltk import sent_tokenize, word_tokenize, pos_tag
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import io
from io import StringIO
import csv
import unicodedata
import string
import plotly
import plotly.express as px
import plot_likert
from sklearn.model_selection import train_test_split
%matplotlib inline
import nbconvert
from nbconvert import HTMLExporter
import nbformat
from wordcloud import WordCloud
from nltk import bigrams
from nltk import trigrams
In [2]:
nltk.download('punkt_tab')
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
[nltk_data] Downloading package punkt_tab to [nltk_data] C:\Users\nazin\AppData\Roaming\nltk_data... [nltk_data] Package punkt_tab is already up-to-date! [nltk_data] Downloading package punkt to [nltk_data] C:\Users\nazin\AppData\Roaming\nltk_data... [nltk_data] Package punkt is already up-to-date! [nltk_data] Downloading package stopwords to [nltk_data] C:\Users\nazin\AppData\Roaming\nltk_data... [nltk_data] Package stopwords is already up-to-date! [nltk_data] Downloading package wordnet to [nltk_data] C:\Users\nazin\AppData\Roaming\nltk_data... [nltk_data] Package wordnet is already up-to-date!
Out[2]:
True
2. Reading the Excel Table¶
The csv file is converted to a padas dataframe
The dataframe table is displyed with ALL columns and rows with cutting any
In [3]:
df = pd.read_csv(r"D:\DataAnalysis\Social_Survey_Questionnaire_for_Beneficiaries_0.csv")
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
In [347]:
type(df)
Out[347]:
pandas.core.frame.DataFrame
In [4]:
#df
In [349]:
df.shape
Out[349]:
(150, 120)
3. Missing values¶
The Pandas Dataframe is checked for mising values
In [9]:
#df.isnull().sum()
3.1 Dropping all the Missing Values¶
In [10]:
#df.isnull().sum().sort_values(ascending=False)
In [11]:
#df.dropna(inplace=True)
4. Deleting Columnns¶
There are 120 columns and 85 columns were removed from the pandas dataframe so as to focus on specific columns that have questtions in regards to livelihood and landscape transfromation
In [12]:
df1=df.drop(df.columns[[1,2,3,4,5,6,7,8,9,10,11,12,13,14,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,116,117,118,119]], axis = 1)
In [13]:
#df1
5. Renaming Columns¶
The headings of the columns were renamed for easy inclusion in the code
In [14]:
df1.columns
Out[14]:
Index(['ObjectID', '6. Ward Name', '7. Sub Project Name',
'8. Type of Livelihood', '9. Size (Lima)', '12. Name of main project',
'13. Do you represent other beneficiaries?',
'14. How many beneficiaries do you represent?',
'45. Are there cultural practices that hinder the sustainable management of Forests, Wetlands, National Game Parks and Biodiversity?',
'46. What reasons can you give for your answer above?',
'47. Do you think some cultural practices can be changed?',
'48. Do you consider cultural aspects when formulating the livelihood projects?',
'49. What reasons can you give for your answer above?',
'50. What is the main purpose of landscapes (Forests, Water Bodies, Wetlands… etc.) in your livelihood?',
'51. Do you think there is need to measure indicators when managing landscapes?',
'52. How is your connection to nature like?',
'53. What reasons can you give for your answer above?',
'54. How long have you worked on this livelihood project?',
'55. Does your livelihood depend on the natural resources for a living?',
'56. Do you consider changing your livelihood strategy in future?',
'57. Do you think it is easier to change your livelihood practices?',
'58. Are you able to give reasons for your answer above in your ability to change your livelihood practices?',
'59. Have the ecosystem services reduced from the inception of the project in your ward?',
'60. Has the deforestation increased in the ward?',
'61. Do you think protected areas are a hindrance to your livelihoods?',
'62. Are there new livelihood projects that you think of that have never been implemented?',
'63. Do you think the livelihood subprojects are contributing to the sustainability of landscapes?',
'64. Which livelihood is a major contributor to landscape transformation?',
'65. What reasons can you give for your answer above?',
'66. Which type of landscape do you depend on much for a livelihood?',
'67. What reasons can you give for your answer above?', 'Specify:.4',
'Specify:.5', 'Specify:.6', 'Specify:.7'],
dtype='object')
In [15]:
df2=df1.rename(columns= {'1. Do you agree to take part in the above study?': 'Part_study',
'2. Do you know that your participation is voluntary and you are free to withdraw anytime?':'Participation_Voluntary',
'3. Do you give permission to the data that emerges to be used by the researchers only in an anonymised form?': 'Anonymised_Form',
'5. Date': 'Date',
'6. Ward Name': 'Ward_Name',
'7. Sub Project Name': 'Sub_Project_name',
'8. Type of Livelihood': 'Livelihood',
'9. Size (Lima)': 'Size',
'12. Name of main project': 'Name_Main_Project',
'13. Do you represent other beneficiaries?': 'Representing_Others',
'14. How many beneficiaries do you represent?': 'Number_Beneficiaries',
'45. Are there cultural practices that hinder the sustainable management of Forests, Wetlands, National Game Parks and Biodiversity?': 'Cultural_Practices_Hinder',
'46. What reasons can you give for your answer above?': 'Cultural_Practices_Hinder_Reason',
'47. Do you think some cultural practices can be changed?': 'Cultural_Practices_Changed',
'48. Do you consider cultural aspects when formulating the livelihood projects?': 'Cultural_Aspects_Considered',
'49. What reasons can you give for your answer above?': 'Cultural_Aspects_Considered_Reasons',
'50. What is the main purpose of landscapes (Forests, Water Bodies, Wetlands… etc.) in your livelihood?': 'Purpose_Landscape',
'51. Do you think there is need to measure indicators when managing landscapes?': 'Measure_Indicators',
'52. How is your connection to nature like?': 'Connection_Nature',
'53. What reasons can you give for your answer above?': 'Connection_Nature_Reasons',
'54. How long have you worked on this livelihood project?': 'Range_Years',
'55. Does your livelihood depend on the natural resources for a living?': 'Livilihood_Depenedent',
'56. Do you consider changing your livelihood strategy in future?': 'Change_Livelihood',
'57. Do you think it is easier to change your livelihood practices?': 'Change_Livelihood_Easy',
'58. Are you able to give reasons for your answer above in your ability to change your livelihood practices?': 'Change_Livelihood_Easy_Reasons',
'59. Have the ecosystem services reduced from the inception of the project in your ward?': 'Ecosystem_Services_Reduced',
'60. Has the deforestation increased in the ward?': 'Deforestaion_Increased',
'61. Do you think protected areas are a hindrance to your livelihoods?': 'Protected_Areas_Hinderarnce_Livelihood',
'62. Are there new livelihood projects that you think of that have never been implemented?': 'New_Livelihood_Projects',
'63. Do you think the livelihood subprojects are contributing to the sustainability of landscapes?': 'Subprojects_Sustainability_Contribution',
'64. Which livelihood is a major contributor to landscape transformation?': 'Contributor_Landscape_Transformation',
'65. What reasons can you give for your answer above?': 'Contributor_Landscape_Transformation_Reasons',
'66. Which type of landscape do you depend on much for a livelihood?': 'Landscape_Depended_Livelihood',
'67. What reasons can you give for your answer above?': 'Landscape_Depeneded_Livelihood_Reasons',
'Specify:.4': 'Purpose_Landscape_Specific',
'Specify:.5': 'Connection_Nature_Specific',
'Specify:.6': 'Contributor_Landscape_Transformation_Specific',
'Specify:.7': 'Landscape_Depended_Livelihood_Specific'})
In [16]:
#df2
6. Selection of Likert Scale Columns¶
The columns that had likert scale responses were group in a single dataframe and they are 12 in number the columns were looking at the following questions as numbered in the questionaire:
- '45. Are there cultural practices that hinder the sustainable management of Forests, Wetlands, National Game Parks and Biodiversity?
- '47. Do you think some cultural practices can be changed?',
- '48. Do you consider cultural aspects when formulating the livelihood projects?',
- '51. Do you think there is need to measure indicators when managing landscapes?',
- '55. Does your livelihood depend on the natural resources for a living?',
- '56. Do you consider changing your livelihood strategy in future?',
- '57. Do you think it is easier to change your livelihood practices?',
- '59. Have the ecosystem services reduced from the inception of the project in your ward?',
- '60. Has the deforestation increased in the ward?',
- '61. Do you think protected areas are a hindrance to your livelihoods?',
- '62. Are there new livelihood projects that you think of that have never been implemented?',
- '63. Do you think the livelihood subprojects are contributing to the sustainability of landscapes?'
In [17]:
df3=df2.drop(df2.columns[[0,1,2,3,4,5,6,7,9,12,13,15,16,17,21,27,28,29,30,31,32,33,34]], axis = 1)
In [18]:
#df3
7. The Number of Responses¶
The number of responses were counted for each column that had a likert scale and the NaN indicates no response
In [19]:
all_counts = df3.apply(pd.Series.value_counts, dropna=False)
In [20]:
all_counts
Out[20]:
| Cultural_Practices_Hinder | Cultural_Practices_Changed | Cultural_Aspects_Considered | Measure_Indicators | Livilihood_Depenedent | Change_Livelihood | Change_Livelihood_Easy | Ecosystem_Services_Reduced | Deforestaion_Increased | Protected_Areas_Hinderarnce_Livelihood | New_Livelihood_Projects | Subprojects_Sustainability_Contribution | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Agree_Likert | 20 | 29 | 19 | 28 | 35 | 39 | 46 | 39 | 16 | 14.0 | 58 | 37 |
| Disagree_Likert | 22 | 22 | 20 | 4 | 25 | 37 | 24 | 13 | 32 | 19.0 | 15 | 4 |
| Strongly_Agree_Likert | 21 | 29 | 22 | 81 | 44 | 38 | 49 | 72 | 72 | 15.0 | 36 | 81 |
| Strongly_Disagree_Likert | 70 | 41 | 66 | 22 | 32 | 20 | 18 | 16 | 17 | 83.0 | 19 | 15 |
| Undecided_Likert | 4 | 9 | 7 | 7 | 4 | 5 | 4 | 3 | 1 | NaN | 10 | 3 |
| NaN | 13 | 20 | 16 | 8 | 10 | 11 | 9 | 7 | 12 | 19.0 | 12 | 10 |
7.1 Transposing the Dataframe Table¶
The columns and the rows were interchanged so that they can be easily presented on the graph
In [21]:
all_counts1 = all_counts.head().T
In [22]:
all_counts1
Out[22]:
| Agree_Likert | Disagree_Likert | Strongly_Agree_Likert | Strongly_Disagree_Likert | Undecided_Likert | |
|---|---|---|---|---|---|
| Cultural_Practices_Hinder | 20.0 | 22.0 | 21.0 | 70.0 | 4.0 |
| Cultural_Practices_Changed | 29.0 | 22.0 | 29.0 | 41.0 | 9.0 |
| Cultural_Aspects_Considered | 19.0 | 20.0 | 22.0 | 66.0 | 7.0 |
| Measure_Indicators | 28.0 | 4.0 | 81.0 | 22.0 | 7.0 |
| Livilihood_Depenedent | 35.0 | 25.0 | 44.0 | 32.0 | 4.0 |
| Change_Livelihood | 39.0 | 37.0 | 38.0 | 20.0 | 5.0 |
| Change_Livelihood_Easy | 46.0 | 24.0 | 49.0 | 18.0 | 4.0 |
| Ecosystem_Services_Reduced | 39.0 | 13.0 | 72.0 | 16.0 | 3.0 |
| Deforestaion_Increased | 16.0 | 32.0 | 72.0 | 17.0 | 1.0 |
| Protected_Areas_Hinderarnce_Livelihood | 14.0 | 19.0 | 15.0 | 83.0 | NaN |
| New_Livelihood_Projects | 58.0 | 15.0 | 36.0 | 19.0 | 10.0 |
| Subprojects_Sustainability_Contribution | 37.0 | 4.0 | 81.0 | 15.0 | 3.0 |
7.2 Changing the Order of Columns¶
The order of columns was changed so that they can be easily analysed
In [23]:
all_counts2 = all_counts1.iloc[:, [3, 1, 4, 0, 2]]
In [24]:
all_counts2
Out[24]:
| Strongly_Disagree_Likert | Disagree_Likert | Undecided_Likert | Agree_Likert | Strongly_Agree_Likert | |
|---|---|---|---|---|---|
| Cultural_Practices_Hinder | 70.0 | 22.0 | 4.0 | 20.0 | 21.0 |
| Cultural_Practices_Changed | 41.0 | 22.0 | 9.0 | 29.0 | 29.0 |
| Cultural_Aspects_Considered | 66.0 | 20.0 | 7.0 | 19.0 | 22.0 |
| Measure_Indicators | 22.0 | 4.0 | 7.0 | 28.0 | 81.0 |
| Livilihood_Depenedent | 32.0 | 25.0 | 4.0 | 35.0 | 44.0 |
| Change_Livelihood | 20.0 | 37.0 | 5.0 | 39.0 | 38.0 |
| Change_Livelihood_Easy | 18.0 | 24.0 | 4.0 | 46.0 | 49.0 |
| Ecosystem_Services_Reduced | 16.0 | 13.0 | 3.0 | 39.0 | 72.0 |
| Deforestaion_Increased | 17.0 | 32.0 | 1.0 | 16.0 | 72.0 |
| Protected_Areas_Hinderarnce_Livelihood | 83.0 | 19.0 | NaN | 14.0 | 15.0 |
| New_Livelihood_Projects | 19.0 | 15.0 | 10.0 | 58.0 | 36.0 |
| Subprojects_Sustainability_Contribution | 15.0 | 4.0 | 3.0 | 37.0 | 81.0 |
7.3 Visualising the Results¶
The results were visualised in form of number of responses
In [25]:
%matplotlib inline
plot_likert.plot_counts(all_counts2, plot_likert.scales.agree, plot_percentage=False, bar_labels=True, bar_labels_color="snow", colors=plot_likert.colors.default_with_darker_neutral)
plt.title("Figure 1: The Total Number of Responses to Variables on Sustainable Transformation", fontsize=14)
plt.show()
C:\Users\nazin\AppData\Local\anaconda3\envs\NLTK_Py_3_12\Lib\site-packages\plot_likert\plot_likert.py:101: FutureWarning: parameter `plot_percentage` for `plot_likert.likert_counts` is deprecated, set it to None and use `compute_percentages` instead warn(
7.4 Converting the Responses to Percentages¶
The responses were converted to percentages
In [26]:
all_counts3 = df3.apply(lambda col: col.value_counts(normalize=True, dropna=False).round(2))
In [27]:
all_counts3
Out[27]:
| Cultural_Practices_Hinder | Cultural_Practices_Changed | Cultural_Aspects_Considered | Measure_Indicators | Livilihood_Depenedent | Change_Livelihood | Change_Livelihood_Easy | Ecosystem_Services_Reduced | Deforestaion_Increased | Protected_Areas_Hinderarnce_Livelihood | New_Livelihood_Projects | Subprojects_Sustainability_Contribution | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Agree_Likert | 0.13 | 0.19 | 0.13 | 0.19 | 0.23 | 0.26 | 0.31 | 0.26 | 0.11 | 0.09 | 0.39 | 0.25 |
| Disagree_Likert | 0.15 | 0.15 | 0.13 | 0.03 | 0.17 | 0.25 | 0.16 | 0.09 | 0.21 | 0.13 | 0.10 | 0.03 |
| Strongly_Agree_Likert | 0.14 | 0.19 | 0.15 | 0.54 | 0.29 | 0.25 | 0.33 | 0.48 | 0.48 | 0.10 | 0.24 | 0.54 |
| Strongly_Disagree_Likert | 0.47 | 0.27 | 0.44 | 0.15 | 0.21 | 0.13 | 0.12 | 0.11 | 0.11 | 0.55 | 0.13 | 0.10 |
| Undecided_Likert | 0.03 | 0.06 | 0.05 | 0.05 | 0.03 | 0.03 | 0.03 | 0.02 | 0.01 | NaN | 0.07 | 0.02 |
| NaN | 0.09 | 0.13 | 0.11 | 0.05 | 0.07 | 0.07 | 0.06 | 0.05 | 0.08 | 0.13 | 0.08 | 0.07 |
7.4.1 Transposing the Dataframe Table¶
Interchanging the rows and columns
In [28]:
all_counts4 = all_counts3.head().T
In [29]:
all_counts4
Out[29]:
| Agree_Likert | Disagree_Likert | Strongly_Agree_Likert | Strongly_Disagree_Likert | Undecided_Likert | |
|---|---|---|---|---|---|
| Cultural_Practices_Hinder | 0.13 | 0.15 | 0.14 | 0.47 | 0.03 |
| Cultural_Practices_Changed | 0.19 | 0.15 | 0.19 | 0.27 | 0.06 |
| Cultural_Aspects_Considered | 0.13 | 0.13 | 0.15 | 0.44 | 0.05 |
| Measure_Indicators | 0.19 | 0.03 | 0.54 | 0.15 | 0.05 |
| Livilihood_Depenedent | 0.23 | 0.17 | 0.29 | 0.21 | 0.03 |
| Change_Livelihood | 0.26 | 0.25 | 0.25 | 0.13 | 0.03 |
| Change_Livelihood_Easy | 0.31 | 0.16 | 0.33 | 0.12 | 0.03 |
| Ecosystem_Services_Reduced | 0.26 | 0.09 | 0.48 | 0.11 | 0.02 |
| Deforestaion_Increased | 0.11 | 0.21 | 0.48 | 0.11 | 0.01 |
| Protected_Areas_Hinderarnce_Livelihood | 0.09 | 0.13 | 0.10 | 0.55 | NaN |
| New_Livelihood_Projects | 0.39 | 0.10 | 0.24 | 0.13 | 0.07 |
| Subprojects_Sustainability_Contribution | 0.25 | 0.03 | 0.54 | 0.10 | 0.02 |
7.4.2 Changing the Order of Columns¶
The order of columns was changed
In [30]:
all_counts5 = all_counts4.iloc[:, [3, 1, 4, 0, 2]]
In [31]:
all_counts5
Out[31]:
| Strongly_Disagree_Likert | Disagree_Likert | Undecided_Likert | Agree_Likert | Strongly_Agree_Likert | |
|---|---|---|---|---|---|
| Cultural_Practices_Hinder | 0.47 | 0.15 | 0.03 | 0.13 | 0.14 |
| Cultural_Practices_Changed | 0.27 | 0.15 | 0.06 | 0.19 | 0.19 |
| Cultural_Aspects_Considered | 0.44 | 0.13 | 0.05 | 0.13 | 0.15 |
| Measure_Indicators | 0.15 | 0.03 | 0.05 | 0.19 | 0.54 |
| Livilihood_Depenedent | 0.21 | 0.17 | 0.03 | 0.23 | 0.29 |
| Change_Livelihood | 0.13 | 0.25 | 0.03 | 0.26 | 0.25 |
| Change_Livelihood_Easy | 0.12 | 0.16 | 0.03 | 0.31 | 0.33 |
| Ecosystem_Services_Reduced | 0.11 | 0.09 | 0.02 | 0.26 | 0.48 |
| Deforestaion_Increased | 0.11 | 0.21 | 0.01 | 0.11 | 0.48 |
| Protected_Areas_Hinderarnce_Livelihood | 0.55 | 0.13 | NaN | 0.09 | 0.10 |
| New_Livelihood_Projects | 0.13 | 0.10 | 0.07 | 0.39 | 0.24 |
| Subprojects_Sustainability_Contribution | 0.10 | 0.03 | 0.02 | 0.25 | 0.54 |
7.4.3 Visualising the Results¶
The results are visualised as pecentages
In [32]:
%matplotlib inline
plot_likert.plot_counts(all_counts5, plot_likert.scales.agree, plot_percentage=True, figsize=(16, 7), bar_labels=True, bar_labels_color="snow", colors=plot_likert.colors.default_with_darker_neutral)
plt.title("Figure 2: The Percentage Number of Responses to Variables on Sustainable Transformation", fontsize=18)
plt.show()
C:\Users\nazin\AppData\Local\anaconda3\envs\NLTK_Py_3_12\Lib\site-packages\plot_likert\plot_likert.py:101: FutureWarning: parameter `plot_percentage` for `plot_likert.likert_counts` is deprecated, set it to None and use `compute_percentages` instead warn(
8. Defined or Responses with Choices¶
The responses that had choices other than those with a likert scale were grouped in a pandas dataframe looking the following question:
- '50. What is the main purpose of landscapes (Forests, Water Bodies, Wetlands… etc.) in your livelihood?': 'Purpose_Landscape',
- '52. How is your connection to nature like?': 'Connection_Nature',
- '54. How long have you worked on this livelihood project?': 'Range_Years',
- '64. Which livelihood is a major contributor to landscape transformation?': 'Contributor_Landscape_Transformation',
- '66. Which type of landscape do you depend on much for a livelihood?': 'Landscape_Depended_Livelihood'
In [33]:
df4=df2.drop(df2.columns[[0,1,2,3,4,5,6,7,8,9,10,11,12,14,16,18,19,20,21,22,23,24,25,26,28,30,31,32,33,34]], axis = 1)
In [34]:
#df4
8.1 The Purpose of Landscape to People¶
In [35]:
PL = df4['Purpose_Landscape'].value_counts(dropna=False)
PL_P = (df4['Purpose_Landscape'].value_counts(normalize=True, dropna=False).round(2))
In [36]:
PL_P
Out[36]:
Purpose_Landscape Nature_Protection_Purpose 0.62 NaN 0.22 Source_Income_Purpose 0.11 Nature_Protection_Purpose,Other_Purpose 0.01 Nature_Protection_Purpose,Ancestral_Shrines_Purpose 0.01 Source_Income_Purpose,Nature_Protection_Purpose 0.01 Nature_Protection_Purpose,Source_Income_Purpose 0.01 Source_Income_Purpose,Other_Purpose 0.01 No_Idea_Purpose 0.01 Other_Purpose 0.01 Name: proportion, dtype: float64
In [37]:
df_PL = pd.DataFrame(PL)
In [38]:
df_PL
Out[38]:
| count | |
|---|---|
| Purpose_Landscape | |
| Nature_Protection_Purpose | 93 |
| NaN | 33 |
| Source_Income_Purpose | 16 |
| Nature_Protection_Purpose,Other_Purpose | 2 |
| Nature_Protection_Purpose,Ancestral_Shrines_Purpose | 1 |
| Source_Income_Purpose,Nature_Protection_Purpose | 1 |
| Nature_Protection_Purpose,Source_Income_Purpose | 1 |
| Source_Income_Purpose,Other_Purpose | 1 |
| No_Idea_Purpose | 1 |
| Other_Purpose | 1 |
In [39]:
ax = sns.countplot(df4["Purpose_Landscape"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 3: Number of Responses on the Purpose of the Landscape", fontsize=14)
plt.show()
8.2 The Connection of Nature to People¶
In [40]:
CN = df4['Connection_Nature'].value_counts(dropna=False)
CN_P = (df4['Connection_Nature'].value_counts(normalize=True, dropna=False).round(2))
In [41]:
CN_P
Out[41]:
Connection_Nature Material_Connection 0.82 NaN 0.09 Other_Connection 0.04 Experiential_Connection 0.01 Philosophical_Connection,Psychological_Connection 0.01 Psychological_Connection,Material_Connection 0.01 Psychological_Connection 0.01 Philosophical_Connection 0.01 Experiential_Connection,Material_Connection 0.01 Name: proportion, dtype: float64
In [42]:
df_CN = pd.DataFrame(CN)
In [43]:
df_CN
Out[43]:
| count | |
|---|---|
| Connection_Nature | |
| Material_Connection | 123 |
| NaN | 13 |
| Other_Connection | 6 |
| Experiential_Connection | 2 |
| Philosophical_Connection,Psychological_Connection | 2 |
| Psychological_Connection,Material_Connection | 1 |
| Psychological_Connection | 1 |
| Philosophical_Connection | 1 |
| Experiential_Connection,Material_Connection | 1 |
In [44]:
ax = sns.countplot(df4["Connection_Nature"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 4: The Number of Responses to Connection to Nature", fontsize=14)
plt.show()
In [46]:
agreement_levels = ["Material_Connection", "Other_Connection"]
CN_R = df2[df2["Connection_Nature"].isin(agreement_levels)]
CN_R1 = CN_R.drop(CN_R.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CN_R1grouped = CN_R1.groupby('Name_Main_Project')['Connection_Nature']
#CN_R1
In [47]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CN_R1.iterrows():
CN_R1_filter_sentence = []
CN_R1_sentence = row["Connection_Nature_Reasons"]
if pd.isnull(CN_R1_sentence):
continue
CN_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CN_R1_sentence)
CN_R1_words = nltk.word_tokenize(CN_R1_sentence_cleaned)
CN_R1_words = [lemmatizer.lemmatize(w) for w in CN_R1_words if w.lower() not in stop_words]
CN_R1_filter_sentence.extend(CN_R1_words)
print(CN_R1_filter_sentence)
['use', 'income'] ['source', 'income', 'future', 'generation', 'see'] ['Source', 'income'] ['source', 'income'] ['Thats', 'get', 'income', 'well', 'example', 'cattle', 'use', 'cultivating'] ['game', 'park', 'help', 'u', 'source', 'income', 'form', 'meat', 'animal', 'tree', 'rain', 'come', 'protected'] ['need', 'keep', 'fish', 'instance', 'fish', 'pond', 'get', 'extinct'] ['one', 'look', 'daily', 'basis'] ['source', 'livelihood'] ['need', 'protect', 'nature', 'instance', 'cutting', 'tree', 'along', 'river', 'lead', 'drying', 'destruction', 'animal', 'biodiversity', 'river'] ['answer'] ['purpose', 'future', 'meet', 'need'] ['source', 'income'] ['tree', 'help', 'u', 'bring', 'rainfall'] ['Source', 'income'] ['harvest', 'right', 'time', 'prevent', 'management', 'natural', 'resource', 'properly'] ['taking', 'care'] ['Source', 'income'] ['Source', 'income'] ['bring', 'development', 'tourism'] ['income'] ['natural', 'resource', 'like', 'tree', 'give', 'shade', 'medicine', 'also', 'get', 'fresh', 'air'] ['Like', 'water', 'source', 'life', 'tree', 'source', 'fresh', 'water'] ['source', 'income'] ['source', 'income'] ['ZAWA', 'Officers', 'one', 'connected', 'look'] ['source', 'income', 'tourism'] ['answer'] ['help', 'u', 'source', 'food', 'well', 'water'] ['tree', 'protected', 'lead', 'rainfall', 'area'] ['source', 'livelihood'] ['Somehow', 'protect'] ['help', 'lot', 'thing', 'air', 'breath'] ['protect', 'u', 'instance', 'tree', 'protect', 'wind', 'bring', 'fresh', 'air', 'well', 'prevent', 'river', 'drying'] ['Thats', 'get', 'honey', 'bee', 'hive', 'u', 'lot', 'money'] ['Thats', 'get', 'free', 'air', 'traditional', 'medicine', 'livelihood', 'depend', 'natural', 'resource'] ['instance', 'fish', 'caught', 'brings', 'income', 'source', 'food', 'well', 'tree', 'bring', 'rainfall', 'indirectly'] []
In [48]:
CN_R1["Connection_Nature_Reasons"] = CN_R1["Connection_Nature_Reasons"].fillna("")
CN_R1["Connection_Nature_Reasons"] = CN_R1["Connection_Nature_Reasons"].astype(str)
CN_R1_Text = " ".join(CN_R1["Connection_Nature_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CN_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Connection Nature", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
In [49]:
CN_R1_bigrams_list = list(CN_R1_filter_sentence)
print(CN_R1_bigrams_list)
#CN_R1_bigram_counts = Counter(zip(bigrams_list, CN_R1_bigrams_list[1:]))
#print(CN_R1_bigram_counts)
#CN_R1_bigrams = pd.DataFrame(CN_R1_bigram_counts.most_common(7),
#columns = ['Word', 'Frequency'])
#print(CN_R1_bigrams)
[]
8.3 The Length of Existence of the Livelihood Project¶
In [50]:
RY_grouped = df2.groupby('Name_Main_Project')['Range_Years'].value_counts(dropna=False)
In [51]:
RY_grouped
Out[51]:
Name_Main_Project Range_Years
EbA_CENTRAL_MUCHINGA_LUAPULA 3to4Years_Long 2
NaN 1
Ecosystem Conservation_NORTH_WESTERN 3to4Years_Long 8
NaN 2
7to8Years_Long 1
PIN_WESTERN 3to4Years_Long 5
NaN 1
SCRALA_SOUTHERN_WESTERN_NORTHEN 3to4Years_Long 12
5to6Years_Long 9
Lessthan2Years_Long 6
Greaterthan9Years_Long 2
7to8Years_Long 1
SCReBS_WESTERN 5to6Years_Long 5
7to8Years_Long 5
Greaterthan9Years_Long 3
3to4Years_Long 2
SCRiKA_LS 5to6Years_Long 31
3to4Years_Long 6
NaN 5
7to8Years_Long 2
TRALARD_LNM 3to4Years_Long 28
5to6Years_Long 5
Lessthan2Years_Long 3
Greaterthan9Years_Long 2
NaN 2
Name: count, dtype: int64
In [52]:
RY_grouped1 = pd.DataFrame(RY_grouped)
In [53]:
RY_grouped1
Out[53]:
| count | ||
|---|---|---|
| Name_Main_Project | Range_Years | |
| EbA_CENTRAL_MUCHINGA_LUAPULA | 3to4Years_Long | 2 |
| NaN | 1 | |
| Ecosystem Conservation_NORTH_WESTERN | 3to4Years_Long | 8 |
| NaN | 2 | |
| 7to8Years_Long | 1 | |
| PIN_WESTERN | 3to4Years_Long | 5 |
| NaN | 1 | |
| SCRALA_SOUTHERN_WESTERN_NORTHEN | 3to4Years_Long | 12 |
| 5to6Years_Long | 9 | |
| Lessthan2Years_Long | 6 | |
| Greaterthan9Years_Long | 2 | |
| 7to8Years_Long | 1 | |
| SCReBS_WESTERN | 5to6Years_Long | 5 |
| 7to8Years_Long | 5 | |
| Greaterthan9Years_Long | 3 | |
| 3to4Years_Long | 2 | |
| SCRiKA_LS | 5to6Years_Long | 31 |
| 3to4Years_Long | 6 | |
| NaN | 5 | |
| 7to8Years_Long | 2 | |
| TRALARD_LNM | 3to4Years_Long | 28 |
| 5to6Years_Long | 5 | |
| Lessthan2Years_Long | 3 | |
| Greaterthan9Years_Long | 2 | |
| NaN | 2 |
In [54]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Lessthan2Years_Long", "3to4Years_Long", "5to6Years_Long", "7to8Years_Long", "Greaterthan9Years_Long", "NaN"]
ax = sns.barplot(data = RY_grouped1, x="count", y="Name_Main_Project", hue="Range_Years", hue_order=hue_order, legend=True)
ax.set_title("Figure 5b: The Number of Livelihood Projects in a Particular Range of Year", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
In [55]:
LP = df4['Range_Years'].value_counts(dropna=False)
LP_P = (df4['Range_Years'].value_counts(normalize=True, dropna=False).round(2))
In [56]:
LP_P
Out[56]:
Range_Years 3to4Years_Long 0.42 5to6Years_Long 0.33 NaN 0.08 Lessthan2Years_Long 0.06 7to8Years_Long 0.06 Greaterthan9Years_Long 0.05 Name: proportion, dtype: float64
In [57]:
df_LP = pd.DataFrame(LP)
In [58]:
df_LP
Out[58]:
| count | |
|---|---|
| Range_Years | |
| 3to4Years_Long | 63 |
| 5to6Years_Long | 50 |
| NaN | 12 |
| Lessthan2Years_Long | 9 |
| 7to8Years_Long | 9 |
| Greaterthan9Years_Long | 7 |
In [59]:
ax = sns.countplot(df4["Range_Years"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 5b: The Number of Livelihood Projects in a Particular Range of Year", fontsize=14)
plt.show()
8.4 Major Contributor to Landscape Transformation¶
In [60]:
CLT_grouped = df2.groupby('Name_Main_Project')['Contributor_Landscape_Transformation'].value_counts(dropna=False)
In [61]:
CLT_grouped
Out[61]:
Name_Main_Project Contributor_Landscape_Transformation
EbA_CENTRAL_MUCHINGA_LUAPULA Land_Agriculture 3
Ecosystem Conservation_NORTH_WESTERN Land_Agriculture 3
Uncontrolled_Fires 3
NaN 3
Uncontrolled_Fires,Land_Agriculture 1
Wood_Extraction 1
PIN_WESTERN Uncontrolled_Fires 3
Wood_Extraction 2
NaN 1
SCRALA_SOUTHERN_WESTERN_NORTHEN Uncontrolled_Fires 19
Wood_Extraction 7
Land_Agriculture 2
NaN 2
SCReBS_WESTERN Uncontrolled_Fires 7
Wood_Extraction 5
Land_Agriculture 2
Other 1
SCRiKA_LS Uncontrolled_Fires 13
Land_Agriculture 12
Wood_Extraction 11
NaN 6
Uncontrolled_Grazing 2
TRALARD_LNM Land_Agriculture 22
Wood_Extraction 13
Uncontrolled_Fires 4
NaN 1
Name: count, dtype: int64
In [62]:
CLT_grouped1 = pd.DataFrame(CLT_grouped)
In [63]:
CLT_grouped1
Out[63]:
| count | ||
|---|---|---|
| Name_Main_Project | Contributor_Landscape_Transformation | |
| EbA_CENTRAL_MUCHINGA_LUAPULA | Land_Agriculture | 3 |
| Ecosystem Conservation_NORTH_WESTERN | Land_Agriculture | 3 |
| Uncontrolled_Fires | 3 | |
| NaN | 3 | |
| Uncontrolled_Fires,Land_Agriculture | 1 | |
| Wood_Extraction | 1 | |
| PIN_WESTERN | Uncontrolled_Fires | 3 |
| Wood_Extraction | 2 | |
| NaN | 1 | |
| SCRALA_SOUTHERN_WESTERN_NORTHEN | Uncontrolled_Fires | 19 |
| Wood_Extraction | 7 | |
| Land_Agriculture | 2 | |
| NaN | 2 | |
| SCReBS_WESTERN | Uncontrolled_Fires | 7 |
| Wood_Extraction | 5 | |
| Land_Agriculture | 2 | |
| Other | 1 | |
| SCRiKA_LS | Uncontrolled_Fires | 13 |
| Land_Agriculture | 12 | |
| Wood_Extraction | 11 | |
| NaN | 6 | |
| Uncontrolled_Grazing | 2 | |
| TRALARD_LNM | Land_Agriculture | 22 |
| Wood_Extraction | 13 | |
| Uncontrolled_Fires | 4 | |
| NaN | 1 |
In [64]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Land_Agriculture", "Wood_Extraction", "Uncontrolled_Fires", "Uncontrolled_Grazing ", "Other", "NaN"]
ax = sns.barplot(data = CLT_grouped1, x="count", y="Name_Main_Project", hue="Contributor_Landscape_Transformation", hue_order=hue_order, legend=True)
ax.set_title("Figure 6a: Major Contributor to Landscape Transformation", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
In [65]:
CLT = df4['Contributor_Landscape_Transformation'].value_counts(dropna=False)
CLT_P = (df4['Contributor_Landscape_Transformation'].value_counts(normalize=True, dropna=False).round(2))
In [66]:
CLT_P
Out[66]:
Contributor_Landscape_Transformation Uncontrolled_Fires 0.33 Land_Agriculture 0.29 Wood_Extraction 0.26 NaN 0.09 Uncontrolled_Grazing 0.01 Uncontrolled_Fires,Land_Agriculture 0.01 Other 0.01 Name: proportion, dtype: float64
In [67]:
df_CLT = pd.DataFrame(CLT)
In [68]:
df_CLT
Out[68]:
| count | |
|---|---|
| Contributor_Landscape_Transformation | |
| Uncontrolled_Fires | 49 |
| Land_Agriculture | 44 |
| Wood_Extraction | 39 |
| NaN | 14 |
| Uncontrolled_Grazing | 2 |
| Uncontrolled_Fires,Land_Agriculture | 1 |
| Other | 1 |
In [69]:
ax = sns.countplot(df4["Contributor_Landscape_Transformation"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 6b: Major Contributor to Landscape Transformation", fontsize=14)
plt.show()
In [71]:
agreement_levels = ["Wood_Extration", "Land_Agriculture","Uncontrolled_Fires"]
CLT_R = df2[df2["Contributor_Landscape_Transformation"].isin(agreement_levels)]
CLT_R1 = CN_R.drop(CLT_R.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,29,30,31,32,33,34]], axis = 1)
CLT_R1grouped = CLT_R1.groupby('Name_Main_Project')['Contributor_Landscape_Transformation']
#CLT_R1
In [72]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CLT_R1.iterrows():
CLT_R1_filter_sentence = []
CLT_R1_sentence = row["Contributor_Landscape_Transformation_Reasons"]
if pd.isnull(CLT_R1_sentence):
continue
CLT_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CLT_R1_sentence)
CLT_R1_words = nltk.word_tokenize(CLT_R1_sentence_cleaned)
CLT_R1_words = [lemmatizer.lemmatize(w) for w in CLT_R1_words if w.lower() not in stop_words]
CLT_R1_filter_sentence.extend(CLT_R1_words)
print(CLT_R1_filter_sentence)
['requires', 'huge', 'land', 'others', 'like', 'making', 'charcoal'] ['burning', 'charcoal', 'give', 'smoke', 'destroys', 'ozone', 'layer', 'well', 'brings', 'acidic', 'rain'] ['agriculture', 'activity', 'food'] ['electricity', 'thus', 'huge', 'demand', 'energy'] ['charcoal', 'purpose', 'bad', 'land', 'left', 'bare', 'unlike', 'agriculture', 'cutting', 'replaced', 'plant'] ['cultivation', 'cassava', 'requires', 'always', 'barren', 'land', 'never', 'cultivated', 'grow', 'well', 'crop', 'bean', 'vegetable', 'maize', 'reduce', 'deforestation'] ['burn', 'tree', 'shoot'] ['Chitemene', 'system', 'cultivation', 'lead', 'deforestation', 'others'] ['source', 'food', 'people', 'area'] ['Thats', 'get', 'livelihood'] ['source', 'income'] ['time', 'cut', 'tree', 'even', 'want', 'cultivate', 'well', 'burining', 'kill', 'animal', 'necesary', 'making', 'soil', 'fertile'] ['Buring', 'destroy', 'product', 'crop', 'soil'] ['cutting', 'treed', 'change', 'landscape'] ['cutting', 'tress', 'destroys', 'much', 'regrowth'] ['u', 'income', 'huge', 'area', 'tree', 'cut'] ['commercial', 'agriculture', 'activity', 'clear', 'huge', 'chuck', 'land'] ['Source', 'income'] ['forming', 'og', 'food', 'crop'] ['source', 'income'] ['people', 'plant', 'huge', 'area', 'land', 'livelihood'] ['animal', 'food', 'well', 'tree', 'would', 'dry'] ['income', 'food', 'crop'] ['farming', 'activity', 'brings', 'income'] ['soil', 'get', 'degraded', 'movebto', 'another', 'portion', 'land'] ['source', 'income'] ['people', 'burn', 'agriculture', 'area', 'looking', 'rat', 'addition', 'burn', 'food', 'crop', 'due', 'search', 'rat'] ['cutting', 'tree', 'destroy', 'change', 'landscape', 'livelihood'] ['tree', 'challenge', 'growung', 'burned'] ['people', 'cutting', 'tree', 'charcoal', 'destroy', 'difficult', 'regeneration'] ['Source', 'income'] ['tree', 'dry', 'burnt'] ['fire', 'cause', 'lot', 'damage', 'biodiversity', 'well', 'plant'] ['tree', 'get', 'burn', 'reducing', 'regeneration', 'well', 'fertility', 'soil'] ['tree', 'get', 'destroyed', 'burnt', 'difficult', 'regenerate'] ['cutting', 'tree', 'charcoal', 'prevents', 'regeneration'] ['cutting', 'tree', 'charcoal', 'requires', 'huge', 'land', 'compared', 'farm', 'one', 'partition', 'land'] ['small', 'biodiversity', 'destroyed', 'fire'] ['Burning', 'destroys', 'tree'] ['fire', 'burn', 'tree', 'lead', 'dry'] ['people', 'cutting', 'huge', 'chuck', 'land', 'cultivation', 'crop'] ['burning', 'lot', 'thing', 'like', 'snake', 'house', 'important', 'biodiversity', 'destroyed'] ['source', 'livelihood'] ['Thats', 'source', 'food', 'crop'] ['people', 'cutting', 'tree', 'anyhow'] ['cutting', 'tree', 'charcoal', 'finish', 'tree', 'others'] ['burning', 'destroys', 'fertility', 'soil'] ['reason', 'told', 'start', 'conservation', 'farming', 'entail', 'farming', 'locality'] ['Chitemene', 'system', 'burning', 'disallowed'] ['main', 'purpose', 'livelihood'] ['fire', 'destroys', 'lot', 'thing', 'air', 'breathe', 'soil', 'fertility', 'small', 'animal', 'plant'] ['land', 'cultivated', 'done', 'big', 'land', 'transforms', 'landscape'] ['fire', 'destroys', 'flower', 'production', 'honey', 'reduced'] ['people', 'make', 'charcoal', 'cut', 'tree', 'fresh', 'cut', 'huge', 'area'] ['destroys', 'lot', 'biodiversity', 'egg', 'bird', 'snake'] ['livelihood', 'depends', 'activity', 'charcoal', 'burning'] ['animal', 'always', 'grazing', 'vegetation', 'room', 'given', 'plant', 'sprout'] ['tree', 'cut', 'charcoal', 'stem', 'dy', 'replacement'] ['Fire', 'destroys', 'everything', 'others', 'even', 'biodiversity', 'get', 'killed'] ['fire', 'cut', 'across', 'huge', 'area', 'kill', 'everything', 'way'] ['land', 'used', 'agriculture', 'purpose', 'cutting', 'done', 'large', 'scale'] ['Burning', 'destroys', 'life', 'everything'] ['fire', 'destroys', 'almost', 'everything'] ['cause', 'soil', 'erossion'] ['agriculture', 'activity', 'uprooting', 'tree', 'thus', 'distruction', 'environment'] ['burning', 'cover', 'huge', 'area', 'kill', 'biodiversity', 'way'] ['making', 'charcoal', 'mainly', 'focus', 'big', 'tree', 'make', 'desert', 'area'] ['destroys', 'tree', 'everyone', 'cutting', 'tree', 'energy'] ['tree', 'cut', 'charcoal', 'take', 'time', 'grow', 'thus', 'causing', 'climate', 'change', 'turn', 'affecting', 'main', 'livelihood', 'agriculture'] ['cut', 'tree', 'rainfall', 'reduce', 'lead', 'animal', 'dying', 'thirst'] ['clear', 'law', 'prevents', 'people', 'scared', 'burning', 'bush'] ['huge', 'land', 'cleared', 'garden'] ['continuous', 'process', 'cutting', 'tree', 'charcoal', 'thus', 'destroys', 'landscape'] ['rainfall', 'reduce', 'would', 'much', 'wind', 'well', 'climate', 'change'] ['tree', 'reduced', 'going', 'problem', 'rainfall'] ['lot', 'famers', 'thus', 'major', 'contributor'] ['Source', 'livelihood', 'crop'] ['farmer'] ['burn', 'bush', 'destroys', 'tree', 'well', 'animal'] ['burning', 'make', 'tree', 'dry', 'well', 'young', 'animal', 'get', 'killed'] ['fire', 'distroys', 'almost', 'everything', 'even', 'bird', 'forest', 'destroyed'] ['tradition', 'cultivation', 'survive', 'tradition', 'burning', 'destroys', 'lot', 'thing', 'hence', 'major', 'contributor'] ['burning', 'bush', 'destroys', 'environment'] ['animal', 'depend', 'grass', 'thus', 'burnt', 'animal', 'would', 'die'] ['depend', 'farming', 'livelihood'] ['charcoal', 'people', 'burn', 'big', 'tree', 'destroys', 'habitat', 'animal'] ['Anyone', 'burn', 'bush', 'would', 'burn', 'forest', 'fire', 'guard', 'prevent', 'forest', 'burnt'] ['knowledge', 'protect', 'environment', 'people', 'thing', 'without', 'knowledge', 'protect', 'environment', 'thus', 'need', 'protect'] ['grass', 'grazing', 'animal', 'would', 'destroyed'] ['destroys', 'food', 'elephant', 'depends', 'burnt'] ['Fire', 'destroys', 'habitat', 'animal'] ['animal', 'feed', 'grass', 'grass', 'burnt', 'animal', 'come', 'community', 'disturb'] ['burn', 'destroys', 'habitat', 'animal'] ['Cutting', 'tree', 'come', 'strong', 'wind'] ['Thats', 'food', 'come'] ['Cutting', 'tree', 'make', 'gardenmatema', 'meaning', 'tree', 'replaced'] ['Fire', 'burn', 'everything', 'term', 'life'] ['fire', 'destroys', 'grazing', 'grass', 'animal'] ['People', 'always', 'burning', 'bush', 'without', 'control'] ['destroys', 'life'] ['Fire', 'burn', 'whole', 'area'] ['Burning', 'kill', 'lot', 'thing', 'biodiversity'] ['tree', 'cut', 'lot', 'thus', 'distructs', 'lot'] ['tree', 'cut', 'grow'] ['cutting', 'tree', 'brings', 'drought'] ['tree', 'cut', 'area', 'possing', 'treat', 'strong', 'wind'] ['rain', 'water', 'flow', 'due', 'lack', 'tree', 'block'] ['lead', 'climate', 'due', 'lack', 'tree'] ['animal', 'graz', 'thus', 'make', 'move', 'long', 'distance', 'find', 'pasture'] ['rainfall', 'back', 'green', 'vegetation', 'thus', 'burnt', 'less', 'rainfall'] ['tree', 'get', 'burnt', 'dry', 'thats', 'reason', 'less', 'rainfall'] ['use', 'Chitemene', 'system', 'cutting', 'tree', 'charcoal'] ['tree', 'cut', 'made', 'charcoal', 'trunk', 'stem', 'grow'] ['destroys', 'everything', 'way'] ['destroys', 'everything', 'path']
In [73]:
CLT_R1["Contributor_Landscape_Transformation_Reasons"] = CLT_R1["Contributor_Landscape_Transformation_Reasons"].fillna("")
CLT_R1["Contributor_Landscape_Transformation_Reasons"] = CLT_R1["Contributor_Landscape_Transformation_Reasons"].astype(str)
CLT_R1_Text = " ".join(CLT_R1["Contributor_Landscape_Transformation_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CLT_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Contributor Landscape Transformation", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
8.5 The Landscape Depended on for a Livelihood¶
In [74]:
LDL_grouped = df2.groupby('Name_Main_Project')['Landscape_Depended_Livelihood'].value_counts(dropna=False)
In [75]:
LDL_grouped
Out[75]:
Name_Main_Project Landscape_Depended_Livelihood
EbA_CENTRAL_MUCHINGA_LUAPULA Agriculture_Areas_Dependent 3
Ecosystem Conservation_NORTH_WESTERN NaN 5
Agriculture_Areas_Dependent 3
Forest_Dependent 2
Wetlands_Dependent 1
PIN_WESTERN Agriculture_Areas_Dependent 5
NaN 1
SCRALA_SOUTHERN_WESTERN_NORTHEN Agriculture_Areas_Dependent 21
Wetlands_Dependent 6
Forest_Dependent 2
NaN 1
SCReBS_WESTERN Agriculture_Areas_Dependent 10
Wetlands_Dependent 5
SCRiKA_LS Agriculture_Areas_Dependent 30
Forest_Dependent 4
Wetlands_Dependent 4
NaN 4
Wetlands_Dependent,Forest_Dependent 2
TRALARD_LNM Agriculture_Areas_Dependent 20
Wetlands_Dependent 9
Forest_Dependent 5
Wetlands_Dependent,Forest_Dependent 2
Agriculture_Areas_Dependent,Wetlands_Dependent 1
Forest_Dependent,Wetlands_Dependent 1
Wetlands_Dependent,Agriculture_Areas_Dependent 1
NaN 1
Name: count, dtype: int64
In [76]:
LDL_grouped1 = pd.DataFrame(LDL_grouped)
In [77]:
LDL_grouped1
Out[77]:
| count | ||
|---|---|---|
| Name_Main_Project | Landscape_Depended_Livelihood | |
| EbA_CENTRAL_MUCHINGA_LUAPULA | Agriculture_Areas_Dependent | 3 |
| Ecosystem Conservation_NORTH_WESTERN | NaN | 5 |
| Agriculture_Areas_Dependent | 3 | |
| Forest_Dependent | 2 | |
| Wetlands_Dependent | 1 | |
| PIN_WESTERN | Agriculture_Areas_Dependent | 5 |
| NaN | 1 | |
| SCRALA_SOUTHERN_WESTERN_NORTHEN | Agriculture_Areas_Dependent | 21 |
| Wetlands_Dependent | 6 | |
| Forest_Dependent | 2 | |
| NaN | 1 | |
| SCReBS_WESTERN | Agriculture_Areas_Dependent | 10 |
| Wetlands_Dependent | 5 | |
| SCRiKA_LS | Agriculture_Areas_Dependent | 30 |
| Forest_Dependent | 4 | |
| Wetlands_Dependent | 4 | |
| NaN | 4 | |
| Wetlands_Dependent,Forest_Dependent | 2 | |
| TRALARD_LNM | Agriculture_Areas_Dependent | 20 |
| Wetlands_Dependent | 9 | |
| Forest_Dependent | 5 | |
| Wetlands_Dependent,Forest_Dependent | 2 | |
| Agriculture_Areas_Dependent,Wetlands_Dependent | 1 | |
| Forest_Dependent,Wetlands_Dependent | 1 | |
| Wetlands_Dependent,Agriculture_Areas_Dependent | 1 | |
| NaN | 1 |
In [78]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Agriculture_Areas_Dependent", "Wetlands_Dependent", "Forest_Dependent", "NaN"]
ax = sns.barplot(data = LDL_grouped1, x="count", y="Name_Main_Project", hue="Landscape_Depended_Livelihood", hue_order=hue_order, legend=True)
ax.set_title("Figure 7a: Landscape Dependent on by Livelihoods", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
In [79]:
LDL = df4['Landscape_Depended_Livelihood'].value_counts(dropna=False)
LDL_P = (df4['Landscape_Depended_Livelihood'].value_counts(normalize=True, dropna=False).round(2))
In [80]:
LDL_P
Out[80]:
Landscape_Depended_Livelihood Agriculture_Areas_Dependent 0.61 Wetlands_Dependent 0.17 Forest_Dependent 0.09 NaN 0.09 Wetlands_Dependent,Forest_Dependent 0.03 Agriculture_Areas_Dependent,Wetlands_Dependent 0.01 Forest_Dependent,Wetlands_Dependent 0.01 Wetlands_Dependent,Agriculture_Areas_Dependent 0.01 Name: proportion, dtype: float64
In [81]:
df_LDL = pd.DataFrame(LDL)
In [82]:
df_LDL
Out[82]:
| count | |
|---|---|
| Landscape_Depended_Livelihood | |
| Agriculture_Areas_Dependent | 92 |
| Wetlands_Dependent | 25 |
| Forest_Dependent | 13 |
| NaN | 13 |
| Wetlands_Dependent,Forest_Dependent | 4 |
| Agriculture_Areas_Dependent,Wetlands_Dependent | 1 |
| Forest_Dependent,Wetlands_Dependent | 1 |
| Wetlands_Dependent,Agriculture_Areas_Dependent | 1 |
In [83]:
ax = sns.countplot(df4["Landscape_Depended_Livelihood"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 7b: Landscape Dependent on by Livelihoods", fontsize=14)
plt.show()
In [85]:
agreement_levels = ["Agriculture_Areas_Dependent", "Wetlands_Dependent","Forest_Dependent"]
LDL_R = df2[df2["Landscape_Depended_Livelihood"].isin(agreement_levels)]
LDL_R1 = LDL_R.drop(LDL_R.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,31,32,33,34]], axis = 1)
LDL_R1grouped = LDL_R1.groupby('Name_Main_Project')['Landscape_Depended_Livelihood']
#LDL_R1
In [86]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in LDL_R1.iterrows():
LDL_R1_filter_sentence = []
LDL_R1_sentence = row["Landscape_Depeneded_Livelihood_Reasons"]
if pd.isnull(LDL_R1_sentence):
continue
LDL_R1_sentence_cleaned = re.sub(r'[^\w\s]','',LDL_R1_sentence)
LDL_R1_words = nltk.word_tokenize(LDL_R1_sentence_cleaned)
LDL_R1_words = [lemmatizer.lemmatize(w) for w in LDL_R1_words if w.lower() not in stop_words]
LDL_R1_filter_sentence.extend(LDL_R1_words)
print(LDL_R1_filter_sentence)
['cultivates', 'large', 'area', 'land', 'crop', 'sold', 'livelihood'] ['catching', 'fish', 'source', 'income', 'area'] ['Food', 'main', 'source', 'livelihood', 'thus', 'without', 'way', 'earn', 'living'] ['source', 'income', 'well', 'food', 'consumption'] ['farming', 'help', 'sourcing', 'food'] ['Thats', 'source', 'income'] ['source', 'livelihood'] ['income', 'come', 'living'] ['help', 'u', 'cultivate', 'cassava', 'maize', 'much', 'fishing', 'activity'] ['farming', 'obtain', 'food', 'consumption', 'household', 'level'] ['food', 'security', 'come'] ['tree', 'bring', 'rainfall', 'area', 'compared', 'area'] ['source', 'income'] ['majority', 'people', 'cultivate', 'land', 'livelihood', 'depending', 'buying', 'shop'] ['less', 'rainfall', 'help', 'u', 'source', 'water'] ['Thats', 'whats', 'common', 'within', 'area'] ['source', 'traditional', 'medicine'] ['provides', 'water', 'drinking', 'water', 'life'] ['Water', 'source', 'life'] ['source', 'food', 'crop', 'income'] ['use', 'cultivation'] ['Source', 'income'] ['mostly', 'farmer'] ['Thats', 'source', 'income', 'livelihood'] ['lot', 'activity', 'come', 'water'] ['food', 'crop'] ['food', 'crop', 'come'] ['Thats', 'source', 'food', 'crop', 'well', 'income'] ['Thats', 'grow', 'crop', 'livelihood'] ['livelihood', 'farming', 'main', 'stay'] ['provides', 'resource', 'people', 'term', 'wood'] ['obtain', 'food', 'crop', 'assist', 'u', 'livelihood'] ['Water', 'life', 'used', 'water', 'environment', 'tree', 'grow', 'prevent', 'developing', 'desert'] ['source', 'crop', 'food'] ['Thats', 'grow', 'crop', 'livelihood'] ['Source', 'life'] ['portion', 'land', 'cultivating', 'one', 'area', 'thus', 'cutting', 'tree'] ['Thats', 'get', 'food', 'product', 'livelihood'] ['Water', 'life'] ['Water', 'life'] ['Water', 'life'] ['answer'] ['crop', 'income', 'come'] ['Thats', 'get', 'food', 'eat'] ['Source', 'livelihood'] ['Life', 'water'] ['Water', 'life', 'evryone', 'drink'] ['income', 'food', 'crop', 'come'] ['tree', 'rain', 'much', 'well', 'soil', 'fertile'] ['Water', 'life'] ['Thats', 'get', 'food', 'crop', 'survival'] ['every', 'individual', 'depend', 'agriculture', 'land'] ['Water', 'life', 'thus', 'water', 'plant', 'dry'] ['source', 'mushroom', 'catapilars', 'forest', 'protected', 'well', 'working', 'honarary', 'officer'] ['Water', 'life', 'used', 'every', 'situation', 'cultivation', 'watering', 'garden'] ['brings', 'u', 'food', 'well', 'income'] ['flood', 'cattle', 'go', 'forest', 'area', 'graz'] ['Thats', 'get', 'food'] ['farmer'] ['farm', 'product'] ['Thats', 'get', 'food', 'crop'] ['Thats', 'get', 'food', 'crop'] ['get', 'food', 'crop', 'livelihood', 'dependent'] ['livelihood', 'based', 'farming'] ['source', 'source', 'livelihood'] ['get', 'food', 'crop', 'livehoods'] ['area', 'farming', 'found', 'forest', 'area'] ['cattle', 'graze'] ['cultivate', 'source', 'food', 'crop', 'game', 'park', 'help', 'depend', 'ZAWA', 'Officers', 'give', 'resource'] ['farmer', 'nature'] ['Thats', 'source', 'food', 'crop'] ['Thats', 'food', 'come'] ['Thats', 'get', 'food', 'eating', 'livelihood'] ['cultivate', 'get', 'crop'] ['food', 'income', 'come', 'help', 'u'] ['Thats', 'get', 'maize', 'staple', 'food', 'farming'] ['keeping', 'bird', 'gardening', 'thus', 'forest', 'protected', 'well', 'animal'] ['found'] ['Source', 'food', 'crop'] ['wetland', 'dry', 'thus', 'depend', 'agriculture', 'food', 'crop'] ['Thats', 'get', 'crop', 'food'] ['food', 'gotten', 'borehole', 'sank'] ['farmer'] ['Thats', 'animal', 'feed'] ['Thats', 'get', 'income'] ['Everything', 'come', 'agriculture'] ['farm', 'crop'] ['Water', 'source', 'life', 'animal', 'cattle'] ['farmer', 'nature'] ['farmer', 'nature', 'southern', 'province'] ['Thats', 'get', 'income'] ['Water', 'life', 'cattle', 'drink', 'water', 'wetland'] ['Water', 'life', 'domesticated', 'animal', 'need', 'water', 'depend', 'animal'] ['Thats', 'get', 'food', 'crop'] ['Thats', 'get', 'food', 'crop', 'income'] ['agriculture', 'get', 'food', 'crop', 'income'] ['major', 'activity', 'around', 'area'] ['food', 'crop'] ['Thats', 'get', 'food', 'living'] ['Thats', 'get', 'food', 'crop'] ['farmer'] ['Thats', 'get', 'food'] ['farming'] ['farmer'] ['Thats', 'food', 'come'] ['farmer'] ['Thats', 'animal', 'graze'] ['flood', 'go', 'leave', 'moisture', 'thats', 'help', 'people', 'grow', 'crop'] ['Thats', 'grow', 'food', 'crop'] ['wetland', 'cultivate', 'rice', 'well', 'get', 'water', 'watering', 'garden'] ['get', 'food', 'eating'] ['Thats', 'grow', 'crop'] ['Thats', 'get', 'food'] ['Thats', 'food', 'crop', 'come'] ['Thats', 'food', 'crop', 'come'] ['Thats', 'get', 'food', 'crop'] ['Thats', 'get', 'food', 'crop'] ['farmer', 'rice'] ['get', 'food', 'crop'] ['cultivate', 'flood', 'plain', 'animal', 'graze'] ['Thats', 'get', 'food'] ['farmer'] ['get', 'crop'] ['farmer'] ['Thats', 'get', 'food', 'money'] ['food', 'crop', 'found'] ['Thats', 'plant', 'maize', 'rice']
In [87]:
LDL_R1["Landscape_Depeneded_Livelihood_Reasons"] = LDL_R1["Landscape_Depeneded_Livelihood_Reasons"].fillna("")
LDL_R1["Landscape_Depeneded_Livelihood_Reasons"] = LDL_R1["Landscape_Depeneded_Livelihood_Reasons"].astype(str)
LDL_R1_Text = " ".join(LDL_R1["Landscape_Depeneded_Livelihood_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(LDL_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Landscape Depeneded Livelihood", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
8.6 Explanation to Choices¶
It gives an explanation to the choice selected that is not among the choices that were provided in the list of the questionnaire
In [88]:
df5=df2.drop(df2.columns[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]], axis = 1)
In [97]:
#df5
In [98]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in df5.iterrows():
PL_RS1_filter_sentence = []
PL_RS1_sentence = row["Purpose_Landscape_Specific"]
if pd.isnull(PL_RS1_sentence):
continue
PL_RS1_sentence_cleaned = re.sub(r'[^\w\s]','',PL_RS1_sentence)
PL_RS1_words = nltk.word_tokenize(PL_RS1_sentence_cleaned)
PL_RS1_words = [lemmatizer.lemmatize(w) for w in PL_RS1_words if w.lower() not in stop_words]
PL_RS1_filter_sentence.extend(PL_RS1_words)
print(PL_RS1_filter_sentence)
['protection', 'nature', 'others', 'important'] ['fire', 'burn', 'biodiversity', 'allow', 'environmental', 'process'] ['tourist', 'bring', 'income', 'term', 'viewing', 'animal'] ['source', 'income', 'tourism']
In [99]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in df5.iterrows():
CN_RS1_filter_sentence = []
CN_RS1_sentence = row["Connection_Nature_Specific"]
if pd.isnull(CN_RS1_sentence):
continue
CN_RS1_sentence_cleaned = re.sub(r'[^\w\s]','',CN_RS1_sentence)
CN_RS1_words = nltk.word_tokenize(CN_RS1_sentence_cleaned)
CN_RS1_words = [lemmatizer.lemmatize(w) for w in CN_RS1_words if w.lower() not in stop_words]
CN_RS1_filter_sentence.extend(CN_RS1_words)
print(CN_RS1_filter_sentence)
['Taking', 'care', 'nature', 'without', 'destroying'] ['Taking', 'care', 'nature'] ['Source', 'good', 'air', 'food', 'product'] ['related'] ['Protection', 'tree'] ['Taking', 'care', 'animal']
In [100]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in df5.iterrows():
CLT_RS1_filter_sentence = []
CLT_RS1_sentence = row["Contributor_Landscape_Transformation_Specific"]
if pd.isnull(CLT_RS1_sentence):
continue
CLT_RS1_sentence_cleaned = re.sub(r'[^\w\s]','',CLT_RS1_sentence)
CLT_RS1_words = nltk.word_tokenize(CLT_RS1_sentence_cleaned)
CLT_RS1_words = [lemmatizer.lemmatize(w) for w in CLT_RS1_words if w.lower() not in stop_words]
CLT_RS1_filter_sentence.extend(CLT_RS1_words)
print(CLT_RS1_filter_sentence)
['Cutting', 'tree', 'sale']
In [101]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in df5.iterrows():
LDL_RS1_filter_sentence = []
LDL_RS1_sentence = row["Landscape_Depended_Livelihood_Specific"]
if pd.isnull(LDL_RS1_sentence):
continue
LDL_RS1_sentence_cleaned = re.sub(r'[^\w\s]','',LDL_RS1_sentence)
LDL_RS1_words = nltk.word_tokenize(LDL_RS1_sentence_cleaned)
LDL_RS1_words = [lemmatizer.lemmatize(w) for w in LDL_RS1_words if w.lower() not in stop_words]
LDL_RS1_filter_sentence.extend(LDL_RS1_words)
print(LDL_RS1_filter_sentence)
9. Description Statistics¶
It gives a background to the study sites
In [102]:
df6=df2.drop(df2.columns[[0,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
In [104]:
#df6
9.1 Main project Types¶
This shows the number of questionaires that were administered to each project type
In [105]:
MP = df6['Name_Main_Project'].value_counts(dropna=False)
In [106]:
MP
Out[106]:
Name_Main_Project SCRiKA_LS 44 TRALARD_LNM 40 SCRALA_SOUTHERN_WESTERN_NORTHEN 30 SCReBS_WESTERN 15 Ecosystem Conservation_NORTH_WESTERN 11 PIN_WESTERN 6 EbA_CENTRAL_MUCHINGA_LUAPULA 3 NaN 1 Name: count, dtype: int64
In [107]:
ax = sns.countplot(df6["Name_Main_Project"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 8: The Total Number of Respondents in each of the Main Project", fontsize=14)
plt.show()
9.2 The Wards¶
The wards that had respodents and their number
In [108]:
WN = df6['Ward_Name'].value_counts(dropna=False)
In [109]:
WN
Out[109]:
Ward_Name Namwala Central ward 17 Omba ward 15 Mbila ward 14 Yeta ward 11 Mwanambuyu ward 11 Lulimala ward 11 Isamba ward 10 Kalobolelwa ward 9 Ntonga ward 8 Kalanga ward 8 Moofwe ward 7 Chitimbwa ward 7 Makuya ward 6 Nachikufu ward 5 Luubwe ward 4 Ntambu ward 4 NaN 2 Silunga ward 1 Name: count, dtype: int64
In [110]:
ax = sns.countplot(df6["Ward_Name"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 9: Number of Respondents in each Ward", fontsize=14)
plt.show()
9.3 Number of beneficiaries¶
It shows the number of households that benefited from the interviewees
In [111]:
NB = df6['Number_Beneficiaries'].value_counts(dropna=False)
In [112]:
NB
Out[112]:
Number_Beneficiaries Greaterthan40People_Many 40 10to20People_Many 25 NaN 25 20to30People_Many 24 30to40People_Many 23 Lessthan10People_Many 13 Name: count, dtype: int64
In [113]:
ax = sns.countplot(df6["Number_Beneficiaries"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 10: Number of household beneficiaries in each Cohort Category", fontsize=14)
plt.show()
9.4 Size of Landscape¶
In [114]:
SIZE = df6['Size'].value_counts(dropna=False)
In [115]:
SIZE
Out[115]:
Size Lessthan4Lima_Size 48 Greaterthan16Lima_Size 40 NotApplicable_Size 31 4to8Lima_Size 13 NaN 10 8to12Lima_Size 5 12to16Lima_Size 3 Name: count, dtype: int64
In [116]:
ax = sns.countplot(df6["Size"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 11: Number of beneficiaries in each Land Size Category", fontsize=14)
plt.show()
9.5 Represing Others¶
It looks at those representing one house and more
In [117]:
RO = df6['Representing_Others'].value_counts(dropna=False)
In [118]:
RO
Out[118]:
Representing_Others yes 125 no 23 NaN 2 Name: count, dtype: int64
In [119]:
ax = sns.countplot(df6["Representing_Others"])
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
ax.set_title("Figure 12: Number of beneficiaries Representing Others", fontsize=14)
plt.show()
10. Reasons¶
The pandas dataframe depicts columns for the reasons to the responses to the likert scales
In [120]:
df7=df2.drop(df2.columns[[0,1,2,3,4,5,6,7,8,10,11,13,14,15,17,18,19,20,22,23,24,25,26,27,29,31,32,33,34]], axis = 1)
In [121]:
#df7
10.1 Reasons for Cultural Practices Hidering¶
The reasons for cultural practices that hinder transfromation
In [124]:
lemmatizer = WordNetLemmatizer()
CPH = df7['Cultural_Practices_Hinder_Reason'].str.lower().str.cat(sep=' ')
CPH_words = nltk.tokenize.word_tokenize(CPH)
CPH_filtered_tokens = [word for word in CPH_words if len(CPH_words) >= 8]
CPH_lemmatized_words = [lemmatizer.lemmatize(word) for word in CPH_filtered_tokens]
CPH_token_counts = Counter(CPH_lemmatized_words)
CPH_columns = pd.DataFrame(CPH_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(CPH_columns)
In [127]:
bigrams_list = list(bigrams(CPH_filtered_tokens))
#print(bigrams_list)
CPH_bigram_counts = Counter(zip(bigrams_list, bigrams_list[1:]))
#print(bigram_counts)
CPH_bigrams = pd.DataFrame(CPH_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CPH_bigrams)
Word Frequency
0 ((there, is), (is, nothing)) 73
1 ((is, nothing), (nothing, there)) 55
2 ((nothing, there), (there, is)) 52
3 ((protect, the), (the, environment)) 6
4 ((to, protect), (protect, the)) 6
5 ((there, is), (is, no)) 5
6 ((there, are), (are, no)) 4
7 ((the, environment), (environment, there)) 4
8 ((is, nothing), (nothing, the)) 4
9 ((customary, practices), (practices, that)) 3
10 ((as, well), (well, as)) 3
11 ((are, no), (no, cultural)) 3
12 ((nothing, there), (there, are)) 3
13 ((destroys, the), (the, environment)) 3
14 ((cutting, down), (down, of)) 3
15 ((environment, there), (there, is)) 3
16 ((is, nothing), (nothing, i)) 3
17 ((``, ''), ('', malende)) 3
18 (('', malende), (malende, '')) 3
19 ((malende, ''), ('', '')) 3
20 ((is, no), (no, cultural)) 2
21 ((there, are), (are, rules)) 2
22 ((there, is), (is, a)) 2
23 ((that, the), (the, bush)) 2
24 ((it, is), (is, not)) 2
25 ((are, customary), (customary, practices)) 2
26 ((a, customary), (customary, practice)) 2
27 ((that, destroys), (destroys, the)) 2
28 ((down, of), (of, trees)) 2
29 ((along, the), (the, river)) 2
30 ((a, long), (long, time)) 2
31 ((but, at), (at, the)) 2
32 ((at, the), (the, moment)) 2
33 ((no, cultural), (cultural, practices)) 2
34 ((the, natural), (natural, resources)) 2
35 ((there, are), (are, places)) 2
36 ((nothing, i), (i, have)) 2
37 ((is, nothing), (nothing, our)) 2
38 ((us, to), (to, protect)) 2
39 ((of, the), (the, environment)) 2
40 ((the, environment), (environment, the)) 2
41 ((on, how), (how, to)) 2
42 ((how, to), (to, protect)) 2
43 ((trees, there), (there, is)) 2
44 ((cut, down), (down, trees)) 2
45 ((called, ``), (``, '')) 2
46 (('', ''), ('', that)) 2
47 ((trees, but), (but, the)) 2
48 ((of, the), (the, trees)) 2
49 ((it, has), (has, never)) 1
50 ((has, never), (never, happed)) 1
51 ((never, happed), (happed, before)) 1
52 ((happed, before), (before, in)) 1
53 ((before, in), (in, his)) 1
54 ((in, his), (his, life)) 1
55 ((his, life), (life, time)) 1
56 ((life, time), (time, there)) 1
57 ((time, there), (there, is)) 1
58 ((there, is), (is, need)) 1
59 ((is, need), (need, to)) 1
60 ((need, to), (to, harvest)) 1
61 ((to, harvest), (harvest, trees)) 1
62 ((harvest, trees), (trees, when)) 1
63 ((trees, when), (when, they)) 1
64 ((when, they), (they, have)) 1
65 ((they, have), (have, fully)) 1
66 ((have, fully), (fully, grown)) 1
In [128]:
trigrams_list = list(trigrams(CPH_filtered_tokens))
#print(bigrams_list)
CPH_trigram_counts = Counter(zip(trigrams_list, trigrams_list[1:]))
#print(bigram_counts)
CPH_trigrams = pd.DataFrame(CPH_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CPH_trigrams)
Word \
0 ((there, is, nothing), (is, nothing, there))
1 ((is, nothing, there), (nothing, there, is))
2 ((nothing, there, is), (there, is, nothing))
3 ((to, protect, the), (protect, the, environment))
4 ((there, is, nothing), (is, nothing, the))
5 ((there, are, no), (are, no, cultural))
6 ((is, nothing, there), (nothing, there, are))
7 ((the, environment, there), (environment, there, is))
8 ((environment, there, is), (there, is, nothing))
9 ((there, is, nothing), (is, nothing, i))
10 ((``, '', malende), ('', malende, ''))
11 (('', malende, ''), (malende, '', ''))
12 ((nothing, there, is), (there, is, no))
13 ((there, is, no), (is, no, cultural))
14 ((are, customary, practices), (customary, practices, that))
15 ((that, destroys, the), (destroys, the, environment))
16 ((destroys, the, environment), (the, environment, there))
17 ((cutting, down, of), (down, of, trees))
18 ((but, at, the), (at, the, moment))
19 ((are, no, cultural), (no, cultural, practices))
20 ((is, nothing, i), (nothing, i, have))
21 ((there, is, nothing), (is, nothing, our))
22 ((us, to, protect), (to, protect, the))
23 ((on, how, to), (how, to, protect))
24 ((how, to, protect), (to, protect, the))
25 ((it, has, never), (has, never, happed))
26 ((has, never, happed), (never, happed, before))
27 ((never, happed, before), (happed, before, in))
28 ((happed, before, in), (before, in, his))
29 ((before, in, his), (in, his, life))
30 ((in, his, life), (his, life, time))
31 ((his, life, time), (life, time, there))
32 ((life, time, there), (time, there, is))
33 ((time, there, is), (there, is, need))
34 ((there, is, need), (is, need, to))
35 ((is, need, to), (need, to, harvest))
36 ((need, to, harvest), (to, harvest, trees))
37 ((to, harvest, trees), (harvest, trees, when))
38 ((harvest, trees, when), (trees, when, they))
39 ((trees, when, they), (when, they, have))
40 ((when, they, have), (they, have, fully))
41 ((they, have, fully), (have, fully, grown))
42 ((have, fully, grown), (fully, grown, the))
43 ((fully, grown, the), (grown, the, same))
44 ((grown, the, same), (the, same, applies))
45 ((the, same, applies), (same, applies, to))
46 ((same, applies, to), (applies, to, biodiversity))
47 ((applies, to, biodiversity), (to, biodiversity, there))
48 ((to, biodiversity, there), (biodiversity, there, are))
49 ((biodiversity, there, are), (there, are, no))
50 ((there, are, no), (are, no, customary))
51 ((are, no, customary), (no, customary, practices))
52 ((no, customary, practices), (customary, practices, that))
53 ((customary, practices, that), (practices, that, hinder))
54 ((practices, that, hinder), (that, hinder, sustainable))
55 ((that, hinder, sustainable), (hinder, sustainable, management))
56 ((hinder, sustainable, management), (sustainable, management, there))
57 ((sustainable, management, there), (management, there, are))
58 ((management, there, are), (there, are, crop))
59 ((there, are, crop), (are, crop, rotations))
60 ((are, crop, rotations), (crop, rotations, made))
61 ((crop, rotations, made), (rotations, made, on))
62 ((rotations, made, on), (made, on, the))
63 ((made, on, the), (on, the, land))
64 ((on, the, land), (the, land, when))
65 ((the, land, when), (land, when, cultivating))
66 ((land, when, cultivating), (when, cultivating, to))
Frequency
0 54
1 52
2 47
3 5
4 4
5 3
6 3
7 3
8 3
9 3
10 3
11 3
12 2
13 2
14 2
15 2
16 2
17 2
18 2
19 2
20 2
21 2
22 2
23 2
24 2
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
In [129]:
lemmatizer = WordNetLemmatizer()
CAC = df7['Cultural_Aspects_Considered_Reasons'].str.lower().str.cat(sep=' ')
CAC_words = nltk.tokenize.word_tokenize(CAC)
CAC_filtered_tokens = [word for word in CAC_words if len(CAC_words) >= 4]
CAC_lemmatized_words = [lemmatizer.lemmatize(word) for word in CAC_filtered_tokens]
CAC_token_counts = Counter(CAC_lemmatized_words)
CAC_columns = pd.DataFrame(CAC_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CAC_columns)
Word Frequency 0 the 73 1 not 52 2 is 51 3 we 47 4 there 45 5 nothing 39 6 do 36 7 to 23 8 are 23 9 it 17 10 project 15 11 that 13 12 of 13 13 cultural 12 14 have 11 15 can 11 16 included 11 17 they 11 18 no 10 19 in 10 20 a 10 21 because 10 22 traditional 8 23 aspect 7 24 follow 7 25 practice 7 26 and 6 27 be 6 28 if 6 29 our 6 30 for 5 31 from 5 32 include 5 33 considered 5 34 what 4 35 so 4 36 chief 4 37 give 4 38 or 4 39 answer 4 40 authority 4 41 at 3 42 time 3 43 land 3 44 only 3 45 government 3 46 u 3 47 when 3 48 law 3 49 place 3 50 would 3 51 well 3 52 customary 3 53 destroyed 3 54 done 3 55 but 3 56 people 3 57 change 3 58 community 3 59 history 2 60 need 2 61 come 2 62 with 2 63 say 2 64 , 2 65 tree 2 66 cut 2
In [130]:
CAC_bigrams_list = list(bigrams(CAC_filtered_tokens))
#print(bigrams_list)
CAC_bigram_counts = Counter(zip(CAC_bigrams_list, CAC_bigrams_list[1:]))
#print(bigram_counts)
CAC_bigrams = pd.DataFrame(CAC_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CAC_bigrams)
Word Frequency 0 ((there, is), (is, nothing)) 33 1 ((we, do), (do, not)) 26 2 ((is, nothing), (nothing, we)) 9 3 ((nothing, we), (we, do)) 9 4 ((is, nothing), (nothing, there)) 8 5 ((nothing, there), (there, is)) 7 6 ((do, not), (not, do)) 7 7 ((do, not), (not, include)) 5 8 ((they, are), (are, not)) 5 9 ((not, do), (do, it)) 5 10 ((the, cultural), (cultural, aspects)) 5 11 ((are, not), (not, included)) 4 12 ((there, are), (are, no)) 3 13 ((is, nothing), (nothing, the)) 3 14 ((the, traditional), (traditional, authority)) 3 15 ((cultural, aspects), (aspects, are)) 3 16 ((not, included), (included, we)) 3 17 ((included, we), (we, do)) 3 18 ((that, there), (there, is)) 2 19 ((we, only), (only, follow)) 2 20 ((we, have), (have, to)) 2 21 ((has, to), (to, be)) 2 22 ((is, nothing), (nothing, at)) 2 23 ((nothing, at), (at, the)) 2 24 ((at, the), (the, moment)) 2 25 ((we, follow), (follow, the)) 2 26 ((follow, the), (the, laws)) 2 27 ((the, project), (project, can)) 2 28 ((if, there), (there, are)) 2 29 ((are, no), (no, customary)) 2 30 ((no, customary), (customary, practices)) 2 31 ((if, it), (it, is)) 2 32 ((it, is), (is, not)) 2 33 ((not, work), (work, well)) 2 34 ((not, do), (do, that)) 2 35 ((is, nothing), (nothing, no)) 2 36 ((nothing, no), (no, answer)) 2 37 ((do, it), (it, we)) 2 38 ((do, not), (not, have)) 2 39 ((we, always), (always, follow)) 2 40 ((not, include), (include, there)) 2 41 ((include, there), (there, is)) 2 42 ((is, nothing), (nothing, they)) 2 43 ((can, not), (not, be)) 2 44 ((do, not), (not, the)) 2 45 ((are, considered), (considered, because)) 2 46 ((in, the), (the, community)) 2 47 ((is, nothing), (nothing, included)) 2 48 ((nothing, is), (is, considered)) 2 49 ((considered, there), (there, is)) 2 50 ((do, not), (not, not)) 2 51 ((do, not), (not, we)) 2 52 ((aspects, are), (are, not)) 2 53 ((there, is), (is, no)) 1 54 ((is, no), (no, cultural)) 1 55 ((no, cultural), (cultural, history)) 1 56 ((cultural, history), (history, in)) 1 57 ((history, in), (in, the)) 1 58 ((in, the), (the, area)) 1 59 ((the, area), (area, there)) 1 60 ((area, there), (there, is)) 1 61 ((there, is), (is, need)) 1 62 ((is, need), (need, to)) 1 63 ((need, to), (to, have)) 1 64 ((to, have), (have, power)) 1 65 ((have, power), (power, in)) 1 66 ((power, in), (in, what)) 1
In [131]:
CAC_trigrams_list = list(trigrams(CAC_filtered_tokens))
#print(bigrams_list)
CAC_trigram_counts = Counter(zip(CAC_trigrams_list, CAC_trigrams_list[1:]))
#print(bigram_counts)
CAC_trigrams = pd.DataFrame(CAC_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CAC_trigrams)
Word Frequency 0 ((there, is, nothing), (is, nothing, we)) 9 1 ((nothing, we, do), (we, do, not)) 9 2 ((is, nothing, we), (nothing, we, do)) 8 3 ((there, is, nothing), (is, nothing, there)) 7 4 ((is, nothing, there), (nothing, there, is)) 7 5 ((nothing, there, is), (there, is, nothing)) 7 6 ((we, do, not), (do, not, do)) 7 7 ((we, do, not), (do, not, include)) 5 8 ((do, not, do), (not, do, it)) 5 9 ((there, is, nothing), (is, nothing, the)) 3 10 ((the, cultural, aspects), (cultural, aspects, are)) 3 11 ((not, included, we), (included, we, do)) 3 12 ((included, we, do), (we, do, not)) 3 13 ((there, is, nothing), (is, nothing, at)) 2 14 ((is, nothing, at), (nothing, at, the)) 2 15 ((nothing, at, the), (at, the, moment)) 2 16 ((they, are, not), (are, not, included)) 2 17 ((if, there, are), (there, are, no)) 2 18 ((there, are, no), (are, no, customary)) 2 19 ((are, no, customary), (no, customary, practices)) 2 20 ((if, it, is), (it, is, not)) 2 21 ((do, not, do), (not, do, that)) 2 22 ((there, is, nothing), (is, nothing, no)) 2 23 ((is, nothing, no), (nothing, no, answer)) 2 24 ((not, do, it), (do, it, we)) 2 25 ((we, do, not), (do, not, have)) 2 26 ((do, not, include), (not, include, there)) 2 27 ((not, include, there), (include, there, is)) 2 28 ((include, there, is), (there, is, nothing)) 2 29 ((there, is, nothing), (is, nothing, they)) 2 30 ((we, do, not), (do, not, the)) 2 31 ((there, is, nothing), (is, nothing, included)) 2 32 ((considered, there, is), (there, is, nothing)) 2 33 ((we, do, not), (do, not, not)) 2 34 ((we, do, not), (do, not, we)) 2 35 ((cultural, aspects, are), (aspects, are, not)) 2 36 ((aspects, are, not), (are, not, included)) 2 37 ((are, not, included), (not, included, we)) 2 38 ((there, is, no), (is, no, cultural)) 1 39 ((is, no, cultural), (no, cultural, history)) 1 40 ((no, cultural, history), (cultural, history, in)) 1 41 ((cultural, history, in), (history, in, the)) 1 42 ((history, in, the), (in, the, area)) 1 43 ((in, the, area), (the, area, there)) 1 44 ((the, area, there), (area, there, is)) 1 45 ((area, there, is), (there, is, need)) 1 46 ((there, is, need), (is, need, to)) 1 47 ((is, need, to), (need, to, have)) 1 48 ((need, to, have), (to, have, power)) 1 49 ((to, have, power), (have, power, in)) 1 50 ((have, power, in), (power, in, what)) 1 51 ((power, in, what), (in, what, is)) 1 52 ((in, what, is), (what, is, being)) 1 53 ((what, is, being), (is, being, formulated)) 1 54 ((is, being, formulated), (being, formulated, so)) 1 55 ((being, formulated, so), (formulated, so, that)) 1 56 ((formulated, so, that), (so, that, there)) 1 57 ((so, that, there), (that, there, is)) 1 58 ((that, there, is), (there, is, ownership)) 1 59 ((there, is, ownership), (is, ownership, there)) 1 60 ((is, ownership, there), (ownership, there, are)) 1 61 ((ownership, there, are), (there, are, no)) 1 62 ((there, are, no), (are, no, cultural)) 1 63 ((are, no, cultural), (no, cultural, aspects)) 1 64 ((no, cultural, aspects), (cultural, aspects, at)) 1 65 ((cultural, aspects, at), (aspects, at, the)) 1 66 ((aspects, at, the), (at, the, time)) 1
In [132]:
lemmatizer = WordNetLemmatizer()
CoN = df7['Connection_Nature_Reasons'].str.lower().str.cat(sep=' ')
CoN_words = nltk.tokenize.word_tokenize(CoN)
CoN_filtered_tokens = [word for word in CoN_words if len(CoN_words) >= 4]
CoN_lemmatized_words = [lemmatizer.lemmatize(word) for word in CoN_filtered_tokens]
CoN_token_counts = Counter(CoN_lemmatized_words)
CoN_columns = pd.DataFrame(CoN_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(CoN_columns)
In [133]:
CoN_bigrams_list = list(bigrams(CoN_filtered_tokens))
#print(bigrams_list)
CoN_bigram_counts = Counter(zip(CoN_bigrams_list, CoN_bigrams_list[1:]))
#print(bigram_counts)
CoN_bigrams = pd.DataFrame(CoN_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CoN_bigrams)
Word Frequency 0 ((source, of), (of, income)) 11 1 ((a, source), (source, of)) 9 2 ((as, well), (well, as)) 5 3 ((they, are), (are, a)) 5 4 ((are, a), (a, source)) 5 5 ((of, income), (income, they)) 3 6 ((the, natural), (natural, resources)) 3 7 ((the, animals), (animals, can)) 2 8 ((animals, can), (can, be)) 2 9 ((can, be), (be, used)) 2 10 ((the, future), (future, generation)) 2 11 ((income, they), (they, are)) 2 12 ((future, generation), (generation, to)) 2 13 ((them, source), (source, of)) 2 14 ((it, is), (is, a)) 2 15 ((is, a), (a, source)) 2 16 ((there, is), (is, need)) 2 17 ((is, need), (need, to)) 2 18 ((are, the), (the, ones)) 2 19 ((look, after), (after, them)) 2 20 ((source, of), (of, livelihood)) 2 21 ((of, income), (income, the)) 2 22 ((help, us), (us, in)) 2 23 ((are, source), (source, of)) 2 24 ((source, of), (of, food)) 2 25 ((of, food), (food, as)) 2 26 ((food, as), (as, well)) 2 27 ((a, lot), (lot, of)) 2 28 ((thats, where), (where, we)) 2 29 ((where, we), (we, get)) 2 30 ((be, used), (used, by)) 1 31 ((used, by), (by, the)) 1 32 ((by, the), (the, future)) 1 33 ((future, generation), (generation, as)) 1 34 ((generation, as), (as, well)) 1 35 ((as, well), (well, how)) 1 36 ((well, how), (how, the)) 1 37 ((how, the), (the, animals)) 1 38 ((be, used), (used, as)) 1 39 ((used, as), (as, an)) 1 40 ((as, an), (an, example)) 1 41 ((an, example), (example, on)) 1 42 ((example, on), (on, how)) 1 43 ((on, how), (how, people)) 1 44 ((how, people), (people, should)) 1 45 ((people, should), (should, lead)) 1 46 ((should, lead), (lead, their)) 1 47 ((lead, their), (their, life)) 1 48 ((their, life), (life, through)) 1 49 ((life, through), (through, experiments)) 1 50 ((through, experiments), (experiments, he)) 1 51 ((experiments, he), (he, gains)) 1 52 ((he, gains), (gains, knowledge)) 1 53 ((gains, knowledge), (knowledge, on)) 1 54 ((knowledge, on), (on, the)) 1 55 ((on, the), (the, management)) 1 56 ((the, management), (management, of)) 1 57 ((management, of), (of, forest)) 1 58 ((of, forest), (forest, to)) 1 59 ((forest, to), (to, use)) 1 60 ((to, use), (use, some)) 1 61 ((use, some), (some, of)) 1 62 ((some, of), (of, them)) 1 63 ((of, them), (them, for)) 1 64 ((them, for), (for, income)) 1 65 ((for, income), (income, they)) 1 66 ((they, are), (are, important)) 1
In [134]:
CoN_trigrams_list = list(trigrams(CoN_filtered_tokens))
#print(bigrams_list)
CoN_trigram_counts = Counter(zip(CoN_trigrams_list, CoN_trigrams_list[1:]))
#print(bigram_counts)
CoN_trigrams = pd.DataFrame(CoN_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CoN_trigrams)
Word Frequency 0 ((a, source, of), (source, of, income)) 6 1 ((they, are, a), (are, a, source)) 5 2 ((are, a, source), (a, source, of)) 5 3 ((source, of, income), (of, income, they)) 3 4 ((the, animals, can), (animals, can, be)) 2 5 ((animals, can, be), (can, be, used)) 2 6 ((them, source, of), (source, of, income)) 2 7 ((it, is, a), (is, a, source)) 2 8 ((is, a, source), (a, source, of)) 2 9 ((there, is, need), (is, need, to)) 2 10 ((a, source, of), (source, of, livelihood)) 2 11 ((source, of, income), (of, income, the)) 2 12 ((source, of, food), (of, food, as)) 2 13 ((of, food, as), (food, as, well)) 2 14 ((food, as, well), (as, well, as)) 2 15 ((thats, where, we), (where, we, get)) 2 16 ((can, be, used), (be, used, by)) 1 17 ((be, used, by), (used, by, the)) 1 18 ((used, by, the), (by, the, future)) 1 19 ((by, the, future), (the, future, generation)) 1 20 ((the, future, generation), (future, generation, as)) 1 21 ((future, generation, as), (generation, as, well)) 1 22 ((generation, as, well), (as, well, how)) 1 23 ((as, well, how), (well, how, the)) 1 24 ((well, how, the), (how, the, animals)) 1 25 ((how, the, animals), (the, animals, can)) 1 26 ((can, be, used), (be, used, as)) 1 27 ((be, used, as), (used, as, an)) 1 28 ((used, as, an), (as, an, example)) 1 29 ((as, an, example), (an, example, on)) 1 30 ((an, example, on), (example, on, how)) 1 31 ((example, on, how), (on, how, people)) 1 32 ((on, how, people), (how, people, should)) 1 33 ((how, people, should), (people, should, lead)) 1 34 ((people, should, lead), (should, lead, their)) 1 35 ((should, lead, their), (lead, their, life)) 1 36 ((lead, their, life), (their, life, through)) 1 37 ((their, life, through), (life, through, experiments)) 1 38 ((life, through, experiments), (through, experiments, he)) 1 39 ((through, experiments, he), (experiments, he, gains)) 1 40 ((experiments, he, gains), (he, gains, knowledge)) 1 41 ((he, gains, knowledge), (gains, knowledge, on)) 1 42 ((gains, knowledge, on), (knowledge, on, the)) 1 43 ((knowledge, on, the), (on, the, management)) 1 44 ((on, the, management), (the, management, of)) 1 45 ((the, management, of), (management, of, forest)) 1 46 ((management, of, forest), (of, forest, to)) 1 47 ((of, forest, to), (forest, to, use)) 1 48 ((forest, to, use), (to, use, some)) 1 49 ((to, use, some), (use, some, of)) 1 50 ((use, some, of), (some, of, them)) 1 51 ((some, of, them), (of, them, for)) 1 52 ((of, them, for), (them, for, income)) 1 53 ((them, for, income), (for, income, they)) 1 54 ((for, income, they), (income, they, are)) 1 55 ((income, they, are), (they, are, important)) 1 56 ((they, are, important), (are, important, in)) 1 57 ((are, important, in), (important, in, our)) 1 58 ((important, in, our), (in, our, lifes)) 1 59 ((in, our, lifes), (our, lifes, like)) 1 60 ((our, lifes, like), (lifes, like, trees)) 1 61 ((lifes, like, trees), (like, trees, they)) 1 62 ((like, trees, they), (trees, they, provide)) 1 63 ((trees, they, provide), (they, provide, home)) 1 64 ((they, provide, home), (provide, home, for)) 1 65 ((provide, home, for), (home, for, animals)) 1 66 ((home, for, animals), (for, animals, as)) 1
In [136]:
lemmatizer = WordNetLemmatizer()
CLE = df7['Change_Livelihood_Easy_Reasons'].str.lower().str.cat(sep=' ')
CLE_words = nltk.tokenize.word_tokenize(CLE)
CLE_filtered_tokens = [word for word in CLE_words if len(CLE_words) >= 4]
CLE_lemmatized_words = [lemmatizer.lemmatize(word) for word in CLE_filtered_tokens]
CLE_token_counts = Counter(CLE_lemmatized_words)
CLE_columns = pd.DataFrame(CLE_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(CLE_columns)
In [137]:
CLE_bigrams_list = list(bigrams(CLE_filtered_tokens))
#print(bigrams_list)
CLE_bigram_counts = Counter(zip(CLE_bigrams_list, CLE_bigrams_list[1:]))
#print(bigram_counts)
CLE_bigrams = pd.DataFrame(CLE_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CLE_bigrams)
Word Frequency 0 ((as, long), (long, as)) 14 1 ((it, is), (is, a)) 7 2 ((to, change), (change, because)) 6 3 ((long, as), (as, there)) 6 4 ((need, to), (to, change)) 5 5 ((we, can), (can, change)) 5 6 ((we, depend), (depend, on)) 5 7 ((we, do), (do, not)) 5 8 ((there, is), (is, need)) 4 9 ((the, natural), (natural, resources)) 4 10 ((as, well), (well, as)) 4 11 ((it, is), (is, easier)) 4 12 ((if, there), (there, are)) 4 13 ((as, there), (there, is)) 4 14 ((do, not), (not, have)) 4 15 ((is, a), (a, challenge)) 4 16 ((of, climate), (climate, change)) 4 17 ((thus, it), (it, is)) 4 18 ((can, not), (not, change)) 4 19 ((if, there), (there, is)) 3 20 ((can, change), (change, the)) 3 21 ((is, need), (need, to)) 3 22 ((long, as), (as, i)) 3 23 ((as, i), (i, have)) 3 24 ((source, of), (of, income)) 3 25 ((that, we), (we, do)) 3 26 ((is, not), (not, easy)) 3 27 ((i, do), (do, not)) 3 28 ((for, our), (our, livelihoods)) 3 29 ((it, can), (can, be)) 3 30 ((long, as), (as, we)) 3 31 ((it, is), (is, easy)) 3 32 ((difficult, to), (to, change)) 3 33 ((change, it), (it, is)) 3 34 ((to, change), (change, to)) 2 35 ((change, is), (is, easier)) 2 36 ((is, easier), (easier, because)) 2 37 ((to, change), (change, if)) 2 38 ((change, if), (if, there)) 2 39 ((can, be), (be, changed)) 2 40 ((from, natural), (natural, resources)) 2 41 ((not, depend), (depend, on)) 2 42 ((depend, on), (on, the)) 2 43 ((on, the), (the, natural)) 2 44 ((have, money), (money, for)) 2 45 ((other, livelihoods), (livelihoods, we)) 2 46 ((a, source), (source, of)) 2 47 ((the, world), (world, is)) 2 48 ((we, need), (need, to)) 2 49 ((there, are), (are, some)) 2 50 ((thus, changing), (changing, is)) 2 51 ((do, not), (not, use)) 2 52 ((use, natural), (natural, resources)) 2 53 ((can, not), (not, be)) 2 54 ((so, that), (that, the)) 2 55 ((i, have), (have, some)) 2 56 ((change, because), (because, of)) 2 57 ((to, climate), (climate, change)) 2 58 ((change, no), (no, answer)) 2 59 ((the, livelihoods), (livelihoods, we)) 2 60 ((depend, on), (on, them)) 2 61 ((because, the), (the, livelihood)) 2 62 ((resources, we), (we, can)) 2 63 ((that, we), (we, can)) 2 64 ((we, can), (can, do)) 2 65 ((so, that), (that, we)) 2 66 ((us, act), (act, in)) 2
In [138]:
CLE_trigrams_list = list(trigrams(CLE_filtered_tokens))
#print(bigrams_list)
CLE_trigram_counts = Counter(zip(CLE_trigrams_list, CLE_trigrams_list[1:]))
#print(bigram_counts)
CLE_trigrams = pd.DataFrame(CLE_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CLE_trigrams)
Word Frequency 0 ((as, long, as), (long, as, there)) 6 1 ((long, as, there), (as, there, is)) 4 2 ((it, is, a), (is, a, challenge)) 4 3 ((we, can, change), (can, change, the)) 3 4 ((there, is, need), (is, need, to)) 3 5 ((is, need, to), (need, to, change)) 3 6 ((as, long, as), (long, as, i)) 3 7 ((long, as, i), (as, i, have)) 3 8 ((we, do, not), (do, not, have)) 3 9 ((as, long, as), (long, as, we)) 3 10 ((need, to, change), (to, change, to)) 2 11 ((change, is, easier), (is, easier, because)) 2 12 ((to, change, if), (change, if, there)) 2 13 ((change, if, there), (if, there, is)) 2 14 ((on, the, natural), (the, natural, resources)) 2 15 ((a, source, of), (source, of, income)) 2 16 ((to, change, because), (change, because, of)) 2 17 ((resources, we, can), (we, can, change)) 2 18 ((do, not, have), (not, have, money)) 2 19 ((is, a, challenge), (a, challenge, to)) 2 20 ((a, challenge, to), (challenge, to, change)) 2 21 ((we, only, depend), (only, depend, on)) 2 22 ((livelihood, as, long), (as, long, as)) 2 23 ((long, as, we), (as, we, have)) 2 24 ((thus, i, can), (i, can, not)) 2 25 ((it, as, long), (as, long, as)) 2 26 ((long, as, there), (as, there, are)) 2 27 ((thus, it, is), (it, is, easy)) 2 28 ((it, is, easy), (is, easy, to)) 2 29 ((is, easy, to), (easy, to, change)) 2 30 ((it, is, not), (is, not, easy)) 2 31 ((is, not, easy), (not, easy, but)) 2 32 ((not, easy, but), (easy, but, it)) 2 33 ((easy, but, it), (but, it, is)) 2 34 ((because, of, climate), (of, climate, change)) 2 35 ((thus, it, is), (it, is, a)) 2 36 ((change, as, long), (as, long, as)) 2 37 ((given, to, us), (to, us, by)) 2 38 ((it, is, easier), (is, easier, as)) 2 39 ((is, easier, as), (easier, as, long)) 2 40 ((easier, as, long), (as, long, as)) 2 41 ((as, long, as), (long, as, you)) 2 42 ((if, the, ses), (the, ses, are)) 1 43 ((the, ses, are), (ses, are, taken)) 1 44 ((ses, are, taken), (are, taken, care)) 1 45 ((are, taken, care), (taken, care, of)) 1 46 ((taken, care, of), (care, of, they)) 1 47 ((care, of, they), (of, they, can)) 1 48 ((of, they, can), (they, can, increase)) 1 49 ((they, can, increase), (can, increase, in)) 1 50 ((can, increase, in), (increase, in, number)) 1 51 ((increase, in, number), (in, number, and)) 1 52 ((in, number, and), (number, and, bring)) 1 53 ((number, and, bring), (and, bring, income)) 1 54 ((and, bring, income), (bring, income, the)) 1 55 ((bring, income, the), (income, the, ses)) 1 56 ((income, the, ses), (the, ses, can)) 1 57 ((the, ses, can), (ses, can, be)) 1 58 ((ses, can, be), (can, be, depleted)) 1 59 ((can, be, depleted), (be, depleted, thus)) 1 60 ((be, depleted, thus), (depleted, thus, there)) 1 61 ((depleted, thus, there), (thus, there, need)) 1 62 ((thus, there, need), (there, need, to)) 1 63 ((there, need, to), (need, to, change)) 1 64 ((to, change, to), (change, to, agriculture)) 1 65 ((change, to, agriculture), (to, agriculture, like)) 1 66 ((to, agriculture, like), (agriculture, like, goat)) 1
In [159]:
lemmatizer = WordNetLemmatizer()
CLT = df7['Contributor_Landscape_Transformation_Reasons'].str.lower().str.cat(sep=' ')
CLT_words = nltk.tokenize.word_tokenize(CLT)
CLT_filtered_tokens = [word for word in CLT_words if len(CLT_words) >= 4]
CLT_lemmatized_words = [lemmatizer.lemmatize(word) for word in CLT_filtered_tokens]
CLT_token_counts = Counter(CLT_lemmatized_words)
CLT_columns = pd.DataFrame(CLT_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(CLT_columns)
In [160]:
CLT_bigrams_list = list(bigrams(CLT_filtered_tokens))
#print(bigrams_list)
CLT_bigram_counts = Counter(zip(CLT_bigrams_list, CLT_bigrams_list[1:]))
#print(bigram_counts)
CLT_bigrams = pd.DataFrame(CLT_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CLT_bigrams)
Word Frequency 0 ((as, well), (well, as)) 8 1 ((a, lot), (lot, of)) 6 2 ((source, of), (of, income)) 5 3 ((there, is), (is, no)) 4 4 ((is, the), (the, source)) 4 5 ((the, source), (source, of)) 4 6 ((the, cutting), (cutting, down)) 4 7 ((a, huge), (huge, area)) 4 8 ((trees, for), (for, charcoal)) 4 9 ((lot, of), (of, things)) 4 10 ((the, fire), (fire, destroys)) 4 11 ((it, destroys), (destroys, the)) 4 12 ((when, the), (the, trees)) 4 13 ((the, trees), (trees, are)) 4 14 ((trees, are), (are, cut)) 4 15 ((than, the), (the, others)) 3 16 ((the, burning), (burning, of)) 3 17 ((it, is), (is, a)) 3 18 ((it, is), (is, the)) 3 19 ((cutting, down), (down, of)) 3 20 ((that, is), (is, the)) 3 21 ((well, as), (as, the)) 3 22 ((the, trees), (trees, get)) 3 23 ((cutting, trees), (trees, for)) 3 24 ((are, a), (a, lot)) 3 25 ((destroys, a), (a, lot)) 3 26 ((fire, destroys), (destroys, the)) 3 27 ((destroys, the), (the, habitat)) 3 28 ((the, trees), (trees, have)) 2 29 ((requires, a), (a, huge)) 2 30 ((a, huge), (huge, land)) 2 31 ((the, agriculture), (agriculture, activities)) 2 32 ((that, destroys), (destroys, the)) 2 33 ((it, is), (is, for)) 2 34 ((for, agriculture), (agriculture, purposes)) 2 35 ((brings, income), (income, the)) 2 36 ((the, land), (land, is)) 2 37 ((the, trees), (trees, that)) 2 38 ((is, a), (a, source)) 2 39 ((a, source), (source, of)) 2 40 ((source, of), (of, food)) 2 41 ((in, the), (the, area)) 2 42 ((of, income), (income, some)) 2 43 ((the, soil), (soil, the)) 2 44 ((change, the), (the, landscape)) 2 45 ((the, landscape), (landscape, the)) 2 46 ((huge, chucks), (chucks, of)) 2 47 ((of, land), (land, for)) 2 48 ((for, a), (a, livelihood)) 2 49 ((a, livelihood), (livelihood, the)) 2 50 ((the, animals), (animals, will)) 2 51 ((animals, will), (will, not)) 2 52 ((will, not), (not, have)) 2 53 ((cutting, tree), (tree, for)) 2 54 ((tree, for), (for, charcoal)) 2 55 ((it, is), (is, difficult)) 2 56 ((is, difficult), (difficult, for)) 2 57 ((the, trees), (trees, dry)) 2 58 ((when, they), (they, are)) 2 59 ((they, are), (are, burnt)) 2 60 ((the, fertility), (fertility, of)) 2 61 ((fertility, of), (of, the)) 2 62 ((of, the), (the, soil)) 2 63 ((the, cutting), (cutting, of)) 2 64 ((cutting, of), (of, trees)) 2 65 ((of, trees), (trees, for)) 2 66 ((burning, destroys), (destroys, the)) 2
In [161]:
CLT_trigrams_list = list(trigrams(CLT_filtered_tokens))
#print(bigrams_list)
CLT_trigram_counts = Counter(zip(CLT_trigrams_list, CLT_trigrams_list[1:]))
#print(bigram_counts)
CLT_trigrams = pd.DataFrame(CLT_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CLT_trigrams)
Word Frequency 0 ((is, the, source), (the, source, of)) 4 1 ((the, cutting, down), (cutting, down, of)) 3 2 ((as, well, as), (well, as, the)) 3 3 ((a, lot, of), (lot, of, things)) 3 4 ((destroys, a, lot), (a, lot, of)) 3 5 ((when, the, trees), (the, trees, are)) 3 6 ((the, trees, are), (trees, are, cut)) 3 7 ((requires, a, huge), (a, huge, land)) 2 8 ((it, is, a), (is, a, source)) 2 9 ((is, a, source), (a, source, of)) 2 10 ((the, source, of), (source, of, income)) 2 11 ((source, of, income), (of, income, some)) 2 12 ((that, is, the), (is, the, source)) 2 13 ((for, a, livelihood), (a, livelihood, the)) 2 14 ((the, animals, will), (animals, will, not)) 2 15 ((animals, will, not), (will, not, have)) 2 16 ((cutting, tree, for), (tree, for, charcoal)) 2 17 ((it, is, difficult), (is, difficult, for)) 2 18 ((when, they, are), (they, are, burnt)) 2 19 ((the, fertility, of), (fertility, of, the)) 2 20 ((fertility, of, the), (of, the, soil)) 2 21 ((the, cutting, of), (cutting, of, trees)) 2 22 ((cutting, of, trees), (of, trees, for)) 2 23 ((of, trees, for), (trees, for, charcoal)) 2 24 ((cutting, trees, for), (trees, for, charcoal)) 2 25 ((there, are, a), (are, a, lot)) 2 26 ((are, a, lot), (a, lot, of)) 2 27 ((the, burning, of), (burning, of, the)) 2 28 ((the, fire, destroys), (fire, destroys, the)) 2 29 ((trees, are, cut), (are, cut, for)) 2 30 ((are, cut, for), (cut, for, charcoal)) 2 31 ((a, huge, area), (huge, area, and)) 2 32 ((huge, area, and), (area, and, kills)) 2 33 ((the, rainfall, will), (rainfall, will, reduce)) 2 34 ((to, protect, the), (protect, the, environment)) 2 35 ((destroys, the, habitat), (the, habitat, of)) 2 36 ((it, destroys, everything), (destroys, everything, on)) 2 37 ((destroys, everything, on), (everything, on, its)) 2 38 ((the, trees, have), (trees, have, been)) 1 39 ((trees, have, been), (have, been, depleted)) 1 40 ((have, been, depleted), (been, depleted, and)) 1 41 ((been, depleted, and), (depleted, and, it)) 1 42 ((depleted, and, it), (and, it, has)) 1 43 ((and, it, has), (it, has, caused)) 1 44 ((it, has, caused), (has, caused, reduced)) 1 45 ((has, caused, reduced), (caused, reduced, rainfall)) 1 46 ((caused, reduced, rainfall), (reduced, rainfall, because)) 1 47 ((reduced, rainfall, because), (rainfall, because, i)) 1 48 ((rainfall, because, i), (because, i, have)) 1 49 ((because, i, have), (i, have, to)) 1 50 ((i, have, to), (have, to, clear)) 1 51 ((have, to, clear), (to, clear, the)) 1 52 ((to, clear, the), (clear, the, land)) 1 53 ((clear, the, land), (the, land, for)) 1 54 ((the, land, for), (land, for, agriculture)) 1 55 ((land, for, agriculture), (for, agriculture, to)) 1 56 ((for, agriculture, to), (agriculture, to, plant)) 1 57 ((agriculture, to, plant), (to, plant, crops)) 1 58 ((to, plant, crops), (plant, crops, hence)) 1 59 ((plant, crops, hence), (crops, hence, the)) 1 60 ((crops, hence, the), (hence, the, landscape)) 1 61 ((hence, the, landscape), (the, landscape, change)) 1 62 ((the, landscape, change), (landscape, change, there)) 1 63 ((landscape, change, there), (change, there, would)) 1 64 ((change, there, would), (there, would, a)) 1 65 ((there, would, a), (would, a, reduction)) 1 66 ((would, a, reduction), (a, reduction, in)) 1
In [162]:
lemmatizer = WordNetLemmatizer()
LDL = df7['Landscape_Depeneded_Livelihood_Reasons'].str.lower().str.cat(sep=' ')
LDL_words = nltk.tokenize.word_tokenize(LDL)
LDL_filtered_tokens = [word for word in LDL_words if len(LDL_words) >= 4]
LDL_lemmatized_words = [lemmatizer.lemmatize(word) for word in LDL_filtered_tokens]
LDL_token_counts = Counter(LDL_lemmatized_words)
LDL_columns = pd.DataFrame(LDL_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(LDL_columns)
In [163]:
LDL_bigrams_list = list(bigrams(LDL_filtered_tokens))
#print(bigrams_list)
LDL_bigram_counts = Counter(zip(LDL_bigrams_list, LDL_bigrams_list[1:]))
#print(bigram_counts)
LDL_bigrams = pd.DataFrame(LDL_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(LDL_bigrams)
Word Frequency 0 ((where, we), (we, get)) 17 1 ((thats, where), (where, we)) 16 2 ((we, get), (get, food)) 16 3 ((a, source), (source, of)) 12 4 ((water, is), (is, life)) 11 5 ((thats, were), (were, we)) 10 6 ((were, we), (we, get)) 9 7 ((we, are), (are, farmers)) 9 8 ((as, well), (well, as)) 7 9 ((get, food), (food, crops)) 7 10 ((source, of), (of, income)) 6 11 ((is, a), (a, source)) 6 12 ((we, get), (get, our)) 6 13 ((food, crops), (crops, thats)) 6 14 ((our, food), (food, crops)) 6 15 ((is, the), (the, source)) 5 16 ((the, source), (source, of)) 5 17 ((source, of), (of, food)) 5 18 ((of, food), (food, crops)) 5 19 ((that, is), (is, where)) 5 20 ((is, where), (where, we)) 5 21 ((crops, thats), (thats, where)) 5 22 ((source, of), (of, livelihood)) 4 23 ((it, is), (is, where)) 4 24 ((food, crops), (crops, and)) 4 25 ((crops, and), (and, income)) 4 26 ((get, our), (our, food)) 4 27 ((are, farmers), (farmers, thats)) 4 28 ((it, is), (is, a)) 3 29 ((is, life), (life, water)) 3 30 ((life, water), (water, is)) 3 31 ((source, of), (of, life)) 3 32 ((for, our), (our, livelihoods)) 3 33 ((food, crops), (crops, come)) 3 34 ((crops, come), (come, from)) 3 35 ((were, we), (we, grow)) 3 36 ((is, life), (life, and)) 3 37 ((crops, for), (for, our)) 3 38 ((comes, from), (from, thats)) 3 39 ((in, the), (the, forest)) 3 40 ((where, we), (we, cultivate)) 3 41 ((we, depend), (depend, on)) 3 42 ((are, farmers), (farmers, by)) 3 43 ((farmers, by), (by, nature)) 3 44 ((crops, thats), (thats, were)) 3 45 ((from, thats), (thats, where)) 3 46 ((get, food), (food, for)) 3 47 ((that, is), (is, were)) 3 48 ((food, crops), (crops, we)) 3 49 ((farmers, thats), (thats, where)) 3 50 ((for, a), (a, livelihood)) 2 51 ((a, livelihood), (livelihood, the)) 2 52 ((there, is), (is, no)) 2 53 ((that, is), (is, a)) 2 54 ((food, for), (for, consumption)) 2 55 ((thats, a), (a, source)) 2 56 ((income, comes), (comes, from)) 2 57 ((for, a), (a, living)) 2 58 ((a, living), (living, it)) 2 59 ((it, helps), (helps, us)) 2 60 ((from, farming), (farming, we)) 2 61 ((we, obtain), (obtain, food)) 2 62 ((where, our), (our, food)) 2 63 ((if, there), (there, is)) 2 64 ((as, a), (a, source)) 2 65 ((they, are), (are, a)) 2 66 ((are, a), (a, source)) 2
In [164]:
LDL_trigrams_list = list(trigrams(LDL_filtered_tokens))
#print(bigrams_list)
LDL_trigram_counts = Counter(zip(LDL_trigrams_list, LDL_trigrams_list[1:]))
#print(bigram_counts)
LDL_trigrams = pd.DataFrame(LDL_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(LDL_trigrams)
Word Frequency 0 ((thats, where, we), (where, we, get)) 14 1 ((where, we, get), (we, get, food)) 11 2 ((we, get, food), (get, food, crops)) 7 3 ((is, a, source), (a, source, of)) 6 4 ((thats, were, we), (were, we, get)) 6 5 ((is, the, source), (the, source, of)) 5 6 ((source, of, food), (of, food, crops)) 5 7 ((were, we, get), (we, get, food)) 4 8 ((where, we, get), (we, get, our)) 4 9 ((we, get, our), (get, our, food)) 4 10 ((food, crops, thats), (crops, thats, where)) 4 11 ((crops, thats, where), (thats, where, we)) 4 12 ((we, are, farmers), (are, farmers, thats)) 4 13 ((a, source, of), (source, of, income)) 3 14 ((it, is, a), (is, a, source)) 3 15 ((water, is, life), (is, life, water)) 3 16 ((is, life, water), (life, water, is)) 3 17 ((a, source, of), (source, of, food)) 3 18 ((food, crops, and), (crops, and, income)) 3 19 ((food, crops, come), (crops, come, from)) 3 20 ((thats, were, we), (were, we, grow)) 3 21 ((water, is, life), (is, life, and)) 3 22 ((that, is, where), (is, where, we)) 3 23 ((we, are, farmers), (are, farmers, by)) 3 24 ((are, farmers, by), (farmers, by, nature)) 3 25 ((we, get, food), (get, food, for)) 3 26 ((get, our, food), (our, food, crops)) 3 27 ((for, a, livelihood), (a, livelihood, the)) 2 28 ((the, source, of), (source, of, income)) 2 29 ((that, is, a), (is, a, source)) 2 30 ((thats, a, source), (a, source, of)) 2 31 ((for, a, living), (a, living, it)) 2 32 ((as, a, source), (a, source, of)) 2 33 ((they, are, a), (are, a, source)) 2 34 ((are, a, source), (a, source, of)) 2 35 ((source, of, traditional), (of, traditional, medicine)) 2 36 ((crops, come, from), (come, from, thats)) 2 37 ((as, well, as), (well, as, income)) 2 38 ((life, and, it), (and, it, is)) 2 39 ((were, we, grow), (we, grow, crops)) 2 40 ((life, water, is), (water, is, life)) 2 41 ((as, well, as), (well, as, the)) 2 42 ((is, where, we), (where, we, farm)) 2 43 ((that, where, we), (where, we, get)) 2 44 ((is, where, we), (where, we, cultivate)) 2 45 ((we, depend, on), (depend, on, the)) 2 46 ((food, crops, thats), (crops, thats, were)) 2 47 ((comes, from, thats), (from, thats, where)) 2 48 ((from, thats, where), (thats, where, we)) 2 49 ((get, food, for), (food, for, eating)) 2 50 ((that, is, were), (is, were, we)) 2 51 ((is, were, we), (were, we, get)) 2 52 ((animals, graze, in), (graze, in, the)) 2 53 ((are, farmers, thats), (farmers, thats, were)) 2 54 ((were, we, get), (we, get, our)) 2 55 ((our, food, crops), (food, crops, thats)) 2 56 ((get, food, crops), (food, crops, and)) 2 57 ((crops, thats, were), (thats, were, we)) 2 58 ((get, food, crops), (food, crops, we)) 2 59 ((food, crops, we), (crops, we, are)) 2 60 ((crops, we, are), (we, are, farmers)) 2 61 ((are, farmers, thats), (farmers, thats, where)) 2 62 ((farmers, thats, where), (thats, where, we)) 2 63 ((from, we, are), (we, are, farmers)) 2 64 ((he, cultivates, a), (cultivates, a, large)) 1 65 ((cultivates, a, large), (a, large, area)) 1 66 ((a, large, area), (large, area, of)) 1
11. Specific Variables¶
11.1 Main Project and Cultural Practices¶
The responses are grouped in line with the main projects
There is also the grouping of responses of those that agreed and storngly agreed in one dataframe as well as those whose responses were strongly disagreed and disagreed
The text responses in form of reasons are tokenized and lammentized
In [165]:
CPH_grouped = df2.groupby('Name_Main_Project')['Cultural_Practices_Hinder'].value_counts(dropna=False)
In [166]:
CPH_grouped
Out[166]:
Name_Main_Project Cultural_Practices_Hinder
EbA_CENTRAL_MUCHINGA_LUAPULA Strongly_Agree_Likert 2
Strongly_Disagree_Likert 1
Ecosystem Conservation_NORTH_WESTERN Strongly_Disagree_Likert 5
Strongly_Agree_Likert 2
NaN 2
Agree_Likert 1
Disagree_Likert 1
PIN_WESTERN Strongly_Disagree_Likert 5
NaN 1
SCRALA_SOUTHERN_WESTERN_NORTHEN Disagree_Likert 11
Strongly_Disagree_Likert 9
Agree_Likert 5
Strongly_Agree_Likert 2
Undecided_Likert 2
NaN 1
SCReBS_WESTERN Strongly_Disagree_Likert 14
Agree_Likert 1
SCRiKA_LS Strongly_Disagree_Likert 20
Disagree_Likert 6
NaN 6
Agree_Likert 5
Strongly_Agree_Likert 5
Undecided_Likert 2
TRALARD_LNM Strongly_Disagree_Likert 16
Strongly_Agree_Likert 10
Agree_Likert 8
Disagree_Likert 4
NaN 2
Name: count, dtype: int64
In [167]:
CPH_grouped1 = pd.DataFrame(CPH_grouped)
In [168]:
CPH_grouped1
Out[168]:
| count | ||
|---|---|---|
| Name_Main_Project | Cultural_Practices_Hinder | |
| EbA_CENTRAL_MUCHINGA_LUAPULA | Strongly_Agree_Likert | 2 |
| Strongly_Disagree_Likert | 1 | |
| Ecosystem Conservation_NORTH_WESTERN | Strongly_Disagree_Likert | 5 |
| Strongly_Agree_Likert | 2 | |
| NaN | 2 | |
| Agree_Likert | 1 | |
| Disagree_Likert | 1 | |
| PIN_WESTERN | Strongly_Disagree_Likert | 5 |
| NaN | 1 | |
| SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | 11 |
| Strongly_Disagree_Likert | 9 | |
| Agree_Likert | 5 | |
| Strongly_Agree_Likert | 2 | |
| Undecided_Likert | 2 | |
| NaN | 1 | |
| SCReBS_WESTERN | Strongly_Disagree_Likert | 14 |
| Agree_Likert | 1 | |
| SCRiKA_LS | Strongly_Disagree_Likert | 20 |
| Disagree_Likert | 6 | |
| NaN | 6 | |
| Agree_Likert | 5 | |
| Strongly_Agree_Likert | 5 | |
| Undecided_Likert | 2 | |
| TRALARD_LNM | Strongly_Disagree_Likert | 16 |
| Strongly_Agree_Likert | 10 | |
| Agree_Likert | 8 | |
| Disagree_Likert | 4 | |
| NaN | 2 |
In [169]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert", "NaN"]
ax = sns.barplot(data = CPH_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Practices_Hinder", hue_order=hue_order, legend=True)
ax.set_title("Figure 13: Number of Responses on cultural Practices Hindering Sustainable Management of SES in the Main Projects", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
In [170]:
agreement_levels = ["Agree_Likert", "Strongly_Agree_Likert"]
CPH_R = df2[df2["Cultural_Practices_Hinder"].isin(agreement_levels)]
In [171]:
CPH_R1 = CPH_R.drop(CPH_R.columns[[0,1,2,3,4,6,7,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CPH_R1grouped = CPH_R1.groupby('Name_Main_Project')['Cultural_Practices_Hinder']
#CPH_R1
In [172]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CPH_R1.iterrows():
CPH_R1_filter_sentence = []
CPH_R1_sentence = row["Cultural_Practices_Hinder_Reason"]
if pd.isnull(CPH_R1_sentence):
continue
CPH_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CPH_R1_sentence)
CPH_R1_words = nltk.word_tokenize(CPH_R1_sentence_cleaned)
CPH_R1_words = [lemmatizer.lemmatize(w) for w in CPH_R1_words if w.lower() not in stop_words]
CPH_R1_filter_sentence.extend(CPH_R1_words)
print(CPH_R1_filter_sentence)
['need', 'harvest', 'tree', 'fully', 'grown', 'applies', 'biodiversity'] ['crop', 'rotation', 'made', 'land', 'cultivating', 'ensure', 'fertility', 'soil'] ['allowed', 'cultivate', 'area', '3', 'year', 'shifting', 'another', 'area'] ['rule', 'chief', 'ensure', 'harvesting', 'period', 'followed', 'well', 'period', 'burning', 'bush'] ['norm', 'bush', 'burned', 'dry', 'bush', 'completely', 'burn', 'also', 'support', 'quick', 'regermination', 'vegetation', 'tree'] ['nothing'] ['never', 'heard', 'learnt'] ['cutting', 'tree', 'along', 'river', 'allowed'] ['act', 'late', 'burning', 'destroys', 'environment', 'people', 'community', 'would', 'want', 'catch', 'Catapilars', 'consumption', 'sale'] ['long', 'time', 'ago', 'people', 'poor', 'management', 'natural', 'moment', 'practice', 'maintain', 'biodiversity'] ['cultural', 'practice', 'future', 'generation', 'know', 'anything'] ['customary', 'practice', 'promote', 'early', 'burning', 'people', 'tend', 'burn', 'late'] ['unregulated', 'allocation', 'parcel', 'land', 'forest'] ['Ba', 'chipupila', 'customary', 'practice', 'protecting', 'natural', 'resource'] ['place', 'grave', 'yard', 'protected', 'deforestation'] ['land', 'natural', 'resource', 'located', 'owned', 'traditional', 'authority'] ['Chitemene', 'system', 'destroys', 'environment'] ['experienced', 'cultural', 'practice', 'interfering', 'forest', 'management'] ['old', 'parent', 'taught', 'u', 'protect', 'environment', 'like', 'customary', 'practice'] ['rule', 'made', 'customary', 'practice', 'followed'] ['place', 'allow', 'cutting', 'tree', 'well', 'cutting', 'fruit', 'bearing', 'tree'] ['protection', 'environment', 'resource', 'depleted'] ['cutting', 'tree', 'anyhow'] ['teach', 'u', 'protect', 'environment', 'preventing', 'Chitemene', 'system'] ['always', 'teach', 'people', 'community', 'protect', 'environment'] ['normally', 'give', 'rule', 'protect', 'environment'] ['traditional', 'method', 'harvesting', 'poaching', 'burning', 'cutting', 'tree'] ['people', 'cut', 'tree', 'thus', 'leading', 'wild', 'animal', 'lacking', 'sleep', 'shelter'] ['practice', 'called', 'Malende', 'protect', 'certain', 'area', 'prohibit', 'tree', 'cut'] ['Hynas', 'eat', 'livestock', 'kill', 'conflict', 'ZAWA', 'Officers'] ['ownership', 'land', 'Chief', 'river', 'give', 'power', 'destroy', 'area', 'giving', 'cultivation', 'activity'] ['cultural', 'practice', 'prevent', 'rain', 'falling', 'Malende', 'disturbed'] ['bad', 'fishing', 'method', 'well', 'people', 'settling', 'game', 'park'] ['lack', 'support', 'WDCS', 'CRB', 'local', 'community', 'protect', 'environment', 'license', 'given', 'investor', 'cut', 'tree', 'community', 'benefit', 'sale', 'tree', 'tradition', 'authority', 'benefit'] ['people', 'still', 'want', 'lead', 'life', 'hunting', 'use', 'mosquito', 'net', 'catching', 'fish'] ['traditional', 'leader', 'prohibit', 'people', 'making', 'decision', 'sell', 'land', 'people', 'lead', 'destruction', 'environment', 'even', 'people', 'agree', 'headman', 'headman', 'say', 'land', 'sell', 'want'] ['cultural', 'activity', 'getting', 'root', 'tree', 'medicine', 'destroy', 'tree'] ['culture', 'someone', 'live', 'well', 'need', 'cultivate', 'huge', 'parcel', 'land', 'thus', 'leading', 'cutting', 'tree'] ['Chiefs', 'asking', 'people', 'stop', 'living', 'along', 'river', 'bank', 'using', 'mosquito', 'net', 'fish', 'community', 'adhearing']
In [173]:
CPH_R1["Cultural_Practices_Hinder_Reason"] = CPH_R1["Cultural_Practices_Hinder_Reason"].fillna("")
CPH_R1["Cultural_Practices_Hinder_Reason"] = CPH_R1["Cultural_Practices_Hinder_Reason"].astype(str)
CPH_R1_Text = " ".join(CPH_R1["Cultural_Practices_Hinder_Reason"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CPH_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Cultural Practices Hinder", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
In [174]:
agreement_levels = ["Disagree_Likert", "Strongly_Disagree_Likert"]
CPH_R2 = df2[df2["Cultural_Practices_Hinder"].isin(agreement_levels)]
In [175]:
CPH_R3 = CPH_R2.drop(CPH_R2.columns[[0,1,2,3,4,6,7,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CPH_R3grouped = CPH_R3.groupby('Name_Main_Project')['Cultural_Practices_Hinder']
#CPH_R3
In [176]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CPH_R3.iterrows():
CPH_R3_filter_sentence = []
CPH_R3_sentence = row["Cultural_Practices_Hinder_Reason"]
if pd.isnull(CPH_R3_sentence):
continue
CPH_R3_sentence_cleaned = re.sub(r'[^\w\s]','',CPH_R3_sentence)
CPH_R3_words = nltk.word_tokenize(CPH_R3_sentence_cleaned)
CPH_R3_words = [lemmatizer.lemmatize(w) for w in CPH_R3_words if w.lower() not in stop_words]
CPH_R3_filter_sentence.extend(CPH_R3_words)
print(CPH_R3_filter_sentence)
['never', 'happed', 'life', 'time'] ['customary', 'practice', 'hinder', 'sustainable', 'management'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['cultural', 'history', 'within', 'community', 'except', 'governmnet'] ['cultural', 'norm'] ['nothing'] ['nothing'] ['nothing'] ['customary', 'practice', 'protect', 'environment', 'like', 'way', 'ZAWA', 'protects', 'biodiversity'] ['chitemene', 'system', 'customary', 'practice', 'destroys', 'environment'] ['cultural', 'practice', 'burn', 'bush', 'certain', 'period', 'harvesting'] ['cultural', 'practice'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothinhg'] ['traditional', 'practice', 'government', 'guide', 'u'] ['nothing'] ['nothing'] ['nothing', 'SES', 'looked', 'game', 'park', 'officer', 'officer', 'mandated'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['traditional', 'way', 'management', 'stopped', 'government', 'taken', 'management', 'forest', 'biodiversity'] ['nothing'] ['long', 'time', 'malende', 'used', 'protect', 'tree', 'moment', 'taking', 'place'] ['nothing'] ['nothing'] ['nothing'] ['heard', 'anything', 'like'] ['nothing', 'know', 'hinder'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['thing'] ['nothing'] ['nothing'] ['nothing'] ['Thee', 'nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['hill', 'called', 'Omba', 'release', 'smoke', 'indicating', 'particular', 'year', 'would', 'rainfall', 'evergthing', 'would', 'fine', 'regard', 'rainfall'] ['following', 'law'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['nothing']
In [177]:
CPH_R3["Cultural_Practices_Hinder_Reason"] = CPH_R3["Cultural_Practices_Hinder_Reason"].fillna("")
CPH_R3["Cultural_Practices_Hinder_Reason"] = CPH_R3["Cultural_Practices_Hinder_Reason"].astype(str)
CPH_R3_Text = " ".join(CPH_R3["Cultural_Practices_Hinder_Reason"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CPH_R3_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Cultural Practices Hinder", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
11.2 Main Project and Cultural Practices Changed¶
In [178]:
CPC_grouped = df2.groupby('Name_Main_Project')['Cultural_Practices_Changed'].value_counts(dropna=False)
In [179]:
CPC_grouped
Out[179]:
Name_Main_Project Cultural_Practices_Changed
EbA_CENTRAL_MUCHINGA_LUAPULA Strongly_Agree_Likert 1
Strongly_Disagree_Likert 1
Undecided_Likert 1
Ecosystem Conservation_NORTH_WESTERN NaN 5
Strongly_Agree_Likert 4
Strongly_Disagree_Likert 2
PIN_WESTERN Strongly_Disagree_Likert 4
Strongly_Agree_Likert 1
NaN 1
SCRALA_SOUTHERN_WESTERN_NORTHEN Agree_Likert 11
Disagree_Likert 7
Strongly_Disagree_Likert 4
Undecided_Likert 4
Strongly_Agree_Likert 3
NaN 1
SCReBS_WESTERN Strongly_Disagree_Likert 6
Strongly_Agree_Likert 5
Agree_Likert 4
SCRiKA_LS Agree_Likert 11
Strongly_Agree_Likert 10
NaN 10
Strongly_Disagree_Likert 6
Disagree_Likert 5
Undecided_Likert 2
TRALARD_LNM Strongly_Disagree_Likert 18
Disagree_Likert 10
Strongly_Agree_Likert 5
Agree_Likert 3
Undecided_Likert 2
NaN 2
Name: count, dtype: int64
In [180]:
CPC_grouped1 = pd.DataFrame(CPC_grouped)
In [181]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = CPC_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Practices_Changed", hue_order=hue_order, legend=True)
ax.set_title("Figure 14: Number of Responses on Willingnes to Change Cultural Practices for Sustainable Management of SES in the Main Projects", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
11.3 Main project and Cultural Aspects Considered¶
In [182]:
CAC_grouped = df2.groupby('Name_Main_Project')['Cultural_Aspects_Considered'].value_counts(dropna=False)
In [183]:
CAC_grouped1 = pd.DataFrame(CAC_grouped)
In [184]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = CAC_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Aspects_Considered", hue_order=hue_order, legend=True)
ax.set_title("Figure 15: Number of Responses on if Cultural Aspects are Considered for Sustainable Management of SES in the Main Projects", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
In [185]:
agreement_levels = ["Agree_Likert", "Strongly_Agree_Likert"]
CAC_R = df2[df2["Cultural_Aspects_Considered"].isin(agreement_levels)]
CAC_R1 = CAC_R.drop(CPH_R.columns[[0,1,2,3,4,6,7,8,9,10,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CAC_R1grouped = CAC_R1.groupby('Name_Main_Project')['Cultural_Aspects_Considered']
CAC_R1
Out[185]:
| Name_Main_Project | Cultural_Aspects_Considered | Cultural_Aspects_Considered_Reasons | |
|---|---|---|---|
| 1 | TRALARD_LNM | Strongly_Agree_Likert | There is need to have power in what is being formulated so that there is ownership |
| 3 | TRALARD_LNM | Strongly_Agree_Likert | At the time of getting the land for the project it comes with regulations |
| 7 | TRALARD_LNM | Strongly_Agree_Likert | There are rules that along the river banks, the trees do not need to cut down |
| 11 | TRALARD_LNM | Strongly_Agree_Likert | There are sometimes we have to give the Chiefs from the harvest that we collect or produce that has to be accounted for |
| 17 | TRALARD_LNM | Strongly_Agree_Likert | There is nothing because those things ended a long time ago and the Chiefs would talk to the spirits to protect the land |
| 21 | TRALARD_LNM | Strongly_Agree_Likert | They are not included |
| 22 | TRALARD_LNM | Strongly_Agree_Likert | If there are no customary practices the natural resources can get destroyed |
| 23 | EbA_CENTRAL_MUCHINGA_LUAPULA | Agree_Likert | No answer |
| 24 | TRALARD_LNM | Agree_Likert | No answer |
| 25 | TRALARD_LNM | Agree_Likert | If it is not done the product or project will not work well |
| 27 | TRALARD_LNM | Strongly_Agree_Likert | We follow the customary practices but people burn late which is not inorder |
| 28 | TRALARD_LNM | Strongly_Agree_Likert | We do it so that our livelihood can improve |
| 31 | TRALARD_LNM | Agree_Likert | We just follow the historical practices of our forefathers |
| 32 | TRALARD_LNM | Strongly_Agree_Likert | There cultural practices such as spririal rites done by traditional authority |
| 33 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Agree_Likert | The accessing of land from the traditional authority who also give instructions of where to put livelihoods and not close to the water sources or resources |
| 35 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | A project cannot operate without consulting the traditional authority |
| 39 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | No answer |
| 44 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | Sometimes we discuss locally how the animals are going to be looked after |
| 46 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | We always follow the laws that are taught to us |
| 49 | TRALARD_LNM | Agree_Likert | We can destroy the environment if there are no customary practices |
| 56 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | They cannot be changed because we would like to have the environment protected for our generation to see them in future |
| 57 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | Previously people used to cut trees anyhow but now there is change |
| 59 | Ecosystem Conservation_NORTH_WESTERN | Agree_Likert | The cultural aspects are considered because they are within the CFMG |
| 66 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | Even the Bible says do not forget your roots |
| 68 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | We always follow what we are told |
| 70 | SCRiKA_LS | Strongly_Agree_Likert | We have to consider it because if it is not the projects would not work well for instance in Livingstone the community destroyed the cultural activities of northwestern because they came from a different region |
| 72 | SCRiKA_LS | Strongly_Agree_Likert | The Borehole cannot be sank near the grave yard and when there is funeral in the community you cannot hold a meeting in the village |
| 75 | SCRiKA_LS | Agree_Likert | NaN |
| 76 | SCRiKA_LS | Agree_Likert | The groups have their own mission like our group we give money to the orphans from the profit we make |
| 79 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Agree_Likert | We do not do it |
| 85 | SCRiKA_LS | Strongly_Agree_Likert | We do not |
| 92 | SCRiKA_LS | Agree_Likert | No answer |
| 93 | SCRiKA_LS | Agree_Likert | NaN |
| 94 | SCRiKA_LS | Agree_Likert | NaN |
| 95 | SCRiKA_LS | Agree_Likert | They are considered because the thatched roof, using poles for the cattle craw are some of the cultural aspects that are difficult to change unless people have enough money |
| 100 | SCRiKA_LS | Strongly_Agree_Likert | The project is bettering our lives |
| 109 | SCRiKA_LS | Strongly_Agree_Likert | NaN |
| 110 | SCRiKA_LS | Strongly_Agree_Likert | When we have been taught |
| 115 | SCRiKA_LS | Strongly_Agree_Likert | The traditional leaders have to accept that a certain project has to be done on a specific place |
| 141 | SCReBS_WESTERN | Agree_Likert | We included the induna |
| 146 | PIN_WESTERN | Agree_Likert | Only the indunas that send the representative to learn and they accept the project in the community |
In [484]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CAC_R1.iterrows():
CAC_R1_filter_sentence = []
CAC_R1_sentence = row["Cultural_Aspects_Considered_Reasons"]
if pd.isnull(CAC_R1_sentence):
continue
CAC_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CAC_R1_sentence)
CAC_R1_words = nltk.word_tokenize(CAC_R1_sentence_cleaned)
CAC_R1_words = [lemmatizer.lemmatize(w) for w in CAC_R1_words if w.lower() not in stop_words]
CAC_R1_filter_sentence.extend(CAC_R1_words)
print(CAC_R1_filter_sentence)
['need', 'power', 'formulated', 'ownership'] ['time', 'getting', 'land', 'project', 'come', 'regulation'] ['rule', 'along', 'river', 'bank', 'tree', 'need', 'cut'] ['sometimes', 'give', 'Chiefs', 'harvest', 'collect', 'produce', 'accounted'] ['nothing', 'thing', 'ended', 'long', 'time', 'ago', 'Chiefs', 'would', 'talk', 'spirit', 'protect', 'land'] ['included'] ['customary', 'practice', 'natural', 'resource', 'get', 'destroyed'] ['answer'] ['answer'] ['done', 'product', 'project', 'work', 'well'] ['follow', 'customary', 'practice', 'people', 'burn', 'late', 'inorder'] ['livelihood', 'improve'] ['follow', 'historical', 'practice', 'forefather'] ['cultural', 'practice', 'spririal', 'rite', 'done', 'traditional', 'authority'] ['accessing', 'land', 'traditional', 'authority', 'also', 'give', 'instruction', 'put', 'livelihood', 'close', 'water', 'source', 'resource'] ['project', 'operate', 'without', 'consulting', 'traditional', 'authority'] ['answer'] ['Sometimes', 'discus', 'locally', 'animal', 'going', 'looked'] ['always', 'follow', 'law', 'taught', 'u'] ['destroy', 'environment', 'customary', 'practice'] ['changed', 'would', 'like', 'environment', 'protected', 'generation', 'see', 'future'] ['Previously', 'people', 'used', 'cut', 'tree', 'anyhow', 'change'] ['cultural', 'aspect', 'considered', 'within', 'CFMG'] ['Even', 'Bible', 'say', 'forget', 'root'] ['always', 'follow', 'told'] ['consider', 'project', 'would', 'work', 'well', 'instance', 'Livingstone', 'community', 'destroyed', 'cultural', 'activity', 'northwestern', 'came', 'different', 'region'] ['Borehole', 'sank', 'near', 'grave', 'yard', 'funeral', 'community', 'hold', 'meeting', 'village'] ['group', 'mission', 'like', 'group', 'give', 'money', 'orphan', 'profit', 'make'] [] [] ['answer'] ['considered', 'thatched', 'roof', 'using', 'pole', 'cattle', 'craw', 'cultural', 'aspect', 'difficult', 'change', 'unless', 'people', 'enough', 'money'] ['project', 'bettering', 'life'] ['taught'] ['traditional', 'leader', 'accept', 'certain', 'project', 'done', 'specific', 'place'] ['included', 'induna'] ['indunas', 'send', 'representative', 'learn', 'accept', 'project', 'community']
In [485]:
CAC_R1["Cultural_Aspects_Considered_Reasons"] = CAC_R1["Cultural_Aspects_Considered_Reasons"].fillna("")
CAC_R1["Cultural_Aspects_Considered_Reasons"] = CAC_R1["Cultural_Aspects_Considered_Reasons"].astype(str)
CAC_R1_Text = " ".join(CAC_R1["Cultural_Aspects_Considered_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CAC_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Cultural Aspects Considered", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
In [486]:
agreement_levels = ["Disagree_Likert", "Strongly_Disagree_Likert"]
CAC_R2 = df2[df2["Cultural_Aspects_Considered"].isin(agreement_levels)]
CAC_R3 = CAC_R2.drop(CPH_R2.columns[[0,1,2,3,4,6,7,8,9,10,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CAC_R3grouped = CAC_R3.groupby('Name_Main_Project')['Cultural_Aspects_Considered']
CAC_R3
Out[486]:
| Name_Main_Project | Cultural_Aspects_Considered | Cultural_Aspects_Considered_Reasons | |
|---|---|---|---|
| 0 | TRALARD_LNM | Strongly_Disagree_Likert | There is no cultural history in the area |
| 2 | TRALARD_LNM | Strongly_Disagree_Likert | There are no cultural aspects |
| 4 | TRALARD_LNM | Strongly_Disagree_Likert | We only follow what the chiefs and the government say |
| 5 | TRALARD_LNM | Strongly_Disagree_Likert | There is nothing |
| 6 | TRALARD_LNM | Strongly_Disagree_Likert | There is nothing |
| 8 | TRALARD_LNM | Strongly_Disagree_Likert | There is nothing |
| 9 | TRALARD_LNM | Strongly_Disagree_Likert | There is nothing |
| 10 | TRALARD_LNM | Strongly_Disagree_Likert | The plans were just given to us by TRALARD so there was no consideration of cultural history when formulating the projects |
| 12 | TRALARD_LNM | Strongly_Disagree_Likert | There is nothing |
| 13 | TRALARD_LNM | Strongly_Disagree_Likert | At the moment we follow the laws of the government and not cultural norms |
| 14 | TRALARD_LNM | Strongly_Disagree_Likert | There is nothing |
| 15 | TRALARD_LNM | Strongly_Disagree_Likert | We ask from the headman to give us a place where we can keep goats in a place acceptable to traditional laws |
| 18 | TRALARD_LNM | Disagree_Likert | There is nothing at the moment of having traditional leader doing spritual activities on the projects |
| 19 | TRALARD_LNM | Strongly_Disagree_Likert | We do not include cultural practices |
| 20 | TRALARD_LNM | Strongly_Disagree_Likert | So that the project can move well if the headman is not in the project otherwise if included it might have problems |
| 29 | EbA_CENTRAL_MUCHINGA_LUAPULA | Strongly_Disagree_Likert | They are not available |
| 30 | EbA_CENTRAL_MUCHINGA_LUAPULA | Strongly_Disagree_Likert | We do not consider |
| 34 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | There is nothing |
| 36 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | There is nothing |
| 37 | TRALARD_LNM | Disagree_Likert | We do not do that |
| 38 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | There is nothing |
| 41 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | We do not do it |
| 42 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | We do not do that it as it is unneccesary |
| 45 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | We do not have anything |
| 47 | TRALARD_LNM | Disagree_Likert | We do not do it |
| 50 | TRALARD_LNM | Disagree_Likert | There is nothing |
| 52 | TRALARD_LNM | Disagree_Likert | NaN |
| 53 | TRALARD_LNM | Disagree_Likert | We do not include |
| 55 | TRALARD_LNM | Disagree_Likert | NaN |
| 58 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Disagree_Likert | We do not |
| 62 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Disagree_Likert | There is nothing |
| 67 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | We do not include it in our aspect |
| 71 | SCRiKA_LS | Strongly_Disagree_Likert | There is nothing |
| 74 | SCRiKA_LS | Strongly_Disagree_Likert | There is nothing included |
| 77 | SCRiKA_LS | Strongly_Disagree_Likert | Nothing is considered |
| 80 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | There is nothing |
| 81 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | We do not do it |
| 82 | SCRiKA_LS | Strongly_Disagree_Likert | Nothing is considered |
| 83 | SCRiKA_LS | Strongly_Disagree_Likert | There is nothing |
| 87 | SCRiKA_LS | Disagree_Likert | Not included |
| 88 | SCRiKA_LS | Disagree_Likert | NaN |
| 89 | SCRiKA_LS | Strongly_Disagree_Likert | We do not include |
| 90 | SCRiKA_LS | Strongly_Disagree_Likert | There is nothing included |
| 91 | SCRiKA_LS | Strongly_Disagree_Likert | There is nothing |
| 96 | SCRiKA_LS | Strongly_Disagree_Likert | Not included |
| 97 | SCRiKA_LS | Strongly_Disagree_Likert | Nothing |
| 98 | SCRiKA_LS | Strongly_Disagree_Likert | Nothing |
| 102 | SCRiKA_LS | Disagree_Likert | NaN |
| 103 | SCRiKA_LS | Disagree_Likert | There is nothing |
| 104 | SCRiKA_LS | Strongly_Disagree_Likert | They are under the control of the Chief who can make changes to them |
| 105 | SCRiKA_LS | Disagree_Likert | NaN |
| 106 | SCRiKA_LS | Strongly_Disagree_Likert | There is nothing that is included |
| 107 | SCRiKA_LS | Strongly_Disagree_Likert | They are not taken into consideration |
| 108 | SCRiKA_LS | Strongly_Disagree_Likert | The project can be destroyed because of culture |
| 112 | SCRiKA_LS | Strongly_Disagree_Likert | There is nothing |
| 113 | SCRiKA_LS | Strongly_Disagree_Likert | NaN |
| 114 | SCRiKA_LS | Strongly_Disagree_Likert | We do not |
| 117 | SCRiKA_LS | Strongly_Disagree_Likert | There is nothing |
| 118 | SCRiKA_LS | Disagree_Likert | The projects are not associated with the cultural aspects |
| 119 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | Thee is nothing |
| 120 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | There is nothing |
| 121 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | We do not |
| 122 | SCReBS_WESTERN | Strongly_Disagree_Likert | NaN |
| 123 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | NaN |
| 124 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | Not considered |
| 125 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | There is nothing |
| 127 | SCReBS_WESTERN | Strongly_Disagree_Likert | There is nothing |
| 128 | SCReBS_WESTERN | Disagree_Likert | There is nothing |
| 129 | SCReBS_WESTERN | Strongly_Disagree_Likert | It is because the project does not come through BRE |
| 130 | SCReBS_WESTERN | Strongly_Disagree_Likert | They are not part of the project |
| 131 | SCReBS_WESTERN | Strongly_Disagree_Likert | We do not |
| 132 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | NaN |
| 133 | SCReBS_WESTERN | Strongly_Disagree_Likert | We do not |
| 134 | SCReBS_WESTERN | Strongly_Disagree_Likert | Nothing |
| 135 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | We do not |
| 136 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | There is nothing |
| 137 | SCReBS_WESTERN | Strongly_Disagree_Likert | We do not because we only follow the teachings |
| 138 | SCReBS_WESTERN | Strongly_Disagree_Likert | The cultural aspects are not included |
| 139 | SCReBS_WESTERN | Strongly_Disagree_Likert | The cultural aspects are not included |
| 140 | SCReBS_WESTERN | Strongly_Disagree_Likert | We do not |
| 142 | PIN_WESTERN | Strongly_Disagree_Likert | No we do not |
| 143 | SCReBS_WESTERN | Strongly_Disagree_Likert | They are not included |
| 144 | PIN_WESTERN | Strongly_Disagree_Likert | We do not include |
| 147 | PIN_WESTERN | Strongly_Disagree_Likert | There is nothing |
| 148 | PIN_WESTERN | Strongly_Disagree_Likert | There is nothing |
| 149 | SCReBS_WESTERN | Strongly_Disagree_Likert | We do not do it |
In [487]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CAC_R3.iterrows():
CAC_R3_filter_sentence = []
CAC_R3_sentence = row["Cultural_Aspects_Considered_Reasons"]
if pd.isnull(CAC_R3_sentence):
continue
CAC_R3_sentence_cleaned = re.sub(r'[^\w\s]','',CAC_R3_sentence)
CAC_R3_words = nltk.word_tokenize(CAC_R3_sentence_cleaned)
CAC_R3_words = [lemmatizer.lemmatize(w) for w in CAC_R3_words if w.lower() not in stop_words]
CAC_R3_filter_sentence.extend(CAC_R3_words)
print(CAC_R3_filter_sentence)
['cultural', 'history', 'area'] ['cultural', 'aspect'] ['follow', 'chief', 'government', 'say'] ['nothing'] ['nothing'] ['nothing'] ['nothing'] ['plan', 'given', 'u', 'TRALARD', 'consideration', 'cultural', 'history', 'formulating', 'project'] ['nothing'] ['moment', 'follow', 'law', 'government', 'cultural', 'norm'] ['nothing'] ['ask', 'headman', 'give', 'u', 'place', 'keep', 'goat', 'place', 'acceptable', 'traditional', 'law'] ['nothing', 'moment', 'traditional', 'leader', 'spritual', 'activity', 'project'] ['include', 'cultural', 'practice'] ['project', 'move', 'well', 'headman', 'project', 'otherwise', 'included', 'might', 'problem'] ['available'] ['consider'] ['nothing'] ['nothing'] [] ['nothing'] [] ['unneccesary'] ['anything'] [] ['nothing'] ['include'] [] ['nothing'] ['include', 'aspect'] ['nothing'] ['nothing', 'included'] ['Nothing', 'considered'] ['nothing'] [] ['Nothing', 'considered'] ['nothing'] ['included'] ['include'] ['nothing', 'included'] ['nothing'] ['included'] ['Nothing'] ['Nothing'] ['nothing'] ['control', 'Chief', 'make', 'change'] ['nothing', 'included'] ['taken', 'consideration'] ['project', 'destroyed', 'culture'] ['nothing'] [] ['nothing'] ['project', 'associated', 'cultural', 'aspect'] ['Thee', 'nothing'] ['nothing'] [] ['considered'] ['nothing'] ['nothing'] ['nothing'] ['project', 'come', 'BRE'] ['part', 'project'] [] [] ['Nothing'] [] ['nothing'] ['follow', 'teaching'] ['cultural', 'aspect', 'included'] ['cultural', 'aspect', 'included'] [] [] ['included'] ['include'] ['nothing'] ['nothing'] []
In [488]:
CAC_R3["Cultural_Aspects_Considered_Reasons"] = CAC_R3["Cultural_Aspects_Considered_Reasons"].fillna("")
CAC_R3["Cultural_Aspects_Considered_Reasons"] = CAC_R3["Cultural_Aspects_Considered_Reasons"].astype(str)
CAC_R3_Text = " ".join(CAC_R3["Cultural_Aspects_Considered_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CAC_R3_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Cultural Aspects Considered", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
11.4 Main Project and Measure of Indicators¶
In [489]:
MI_grouped = df2.groupby('Name_Main_Project')['Measure_Indicators'].value_counts(dropna=False)
In [490]:
MI_grouped1 = pd.DataFrame(MI_grouped)
In [491]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = MI_grouped1, x="count", y="Name_Main_Project", hue="Measure_Indicators", hue_order=hue_order, legend=True)
ax.set_title("Figure 16: Number of Responses on if Measurement of Indicators is important for Sustainable Management of SES in the Main Projects", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
11.5 Main Project and Livelihood Dependent¶
In [492]:
LD_grouped = df2.groupby('Name_Main_Project')['Livilihood_Depenedent'].value_counts(dropna=False)
In [493]:
LD_grouped1 = pd.DataFrame(LD_grouped)
In [494]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = LD_grouped1, x="count", y="Name_Main_Project", hue="Livilihood_Depenedent", hue_order=hue_order, legend=True)
ax.set_title("Figure 17: Number of Responses in each of the main Project on if Livelihoods Depend on SES", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
11.6 Main Project and Livelihood Changed¶
In [495]:
CL_grouped = df2.groupby('Name_Main_Project')['Change_Livelihood'].value_counts(dropna=False)
In [496]:
CL_grouped1 = pd.DataFrame(CL_grouped)
In [497]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = CL_grouped1, x="count", y="Name_Main_Project", hue="Change_Livelihood", hue_order=hue_order, legend=True)
ax.set_title("Figure 18: Number of Responses in each of the main Project on if Livelihoods can be Changed", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
11.7 Main Project and Livelihood Changing Easy¶
In [498]:
CLE_grouped = df2.groupby('Name_Main_Project')['Change_Livelihood_Easy'].value_counts(dropna=False)
In [499]:
CLE_grouped1 = pd.DataFrame(CLE_grouped)
In [500]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = CLE_grouped1, x="count", y="Name_Main_Project", hue="Change_Livelihood_Easy", hue_order=hue_order, legend=True)
ax.set_title("Figure 19: Number of Responses in each of the main Project on if Livelihoods can be Changed Easily", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
In [501]:
agreement_levels = ["Agree_Likert", "Strongly_Agree_Likert"]
CLE_R = df2[df2["Change_Livelihood_Easy"].isin(agreement_levels)]
CLE_R1 = CLE_R.drop(CLE_R.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CLE_R1grouped = CLE_R1.groupby('Name_Main_Project')['Change_Livelihood_Easy']
CLE_R1
Out[501]:
| Name_Main_Project | Change_Livelihood_Easy | Change_Livelihood_Easy_Reasons | |
|---|---|---|---|
| 0 | TRALARD_LNM | Strongly_Agree_Likert | If the SES are taken care of they can increase in number and bring income |
| 1 | TRALARD_LNM | Strongly_Agree_Likert | The SeS can be depleted thus there need to change to agriculture like goat rearing |
| 2 | TRALARD_LNM | Agree_Likert | Change is easier because he is the one who has decides to change |
| 4 | TRALARD_LNM | Strongly_Agree_Likert | There has been things like climate change affecting us thus the thought to change |
| 5 | TRALARD_LNM | Strongly_Agree_Likert | If there is new improvement like cooking stove we can change the way of livelihood |
| 6 | TRALARD_LNM | Strongly_Agree_Likert | There can be destruction of SES |
| 7 | TRALARD_LNM | Strongly_Agree_Likert | There is need to change from cutting down to other activities |
| 8 | TRALARD_LNM | Strongly_Agree_Likert | There is need to change to activities that do not destroy the environment |
| 9 | TRALARD_LNM | Strongly_Agree_Likert | They can be changed through learning |
| 11 | TRALARD_LNM | Strongly_Agree_Likert | If we had kept well the projects like chicken and goats that would help to not depend on the natural resources |
| 12 | TRALARD_LNM | Strongly_Agree_Likert | Through the fish ponds of our project |
| 13 | TRALARD_LNM | Strongly_Agree_Likert | Because they can also improve our livelihood in future |
| 14 | TRALARD_LNM | Strongly_Agree_Likert | As long as I have money for other livelihoods |
| 15 | TRALARD_LNM | Strongly_Agree_Likert | We want to venture into farming and that the reason why we shifted to the place we are located |
| 17 | TRALARD_LNM | Strongly_Agree_Likert | NaN |
| 21 | TRALARD_LNM | Strongly_Agree_Likert | There are some sub projects that we do such as bee keeping that can be disturbed by late burning but early burning does not disturb them because the fire is not that much |
| 22 | TRALARD_LNM | Strongly_Agree_Likert | We need to change for instance we depend on water from Lulimala river and if it dries, we will not get water |
| 25 | TRALARD_LNM | Agree_Likert | There natural resources that are protected that cannot be accessed |
| 26 | TRALARD_LNM | Agree_Likert | We use them according to the accepted norms |
| 27 | TRALARD_LNM | Agree_Likert | Yes but we normally use natural resources somehow |
| 32 | TRALARD_LNM | Strongly_Agree_Likert | So that the environment is protected and I have some business that I have |
| 33 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Agree_Likert | It is easier to change because of the experience we have on the natural resources and we change in accordance to climate change |
| 35 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | They can change slowly due to low performance of the group |
| 38 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | We do not depend on them |
| 39 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | It would be difficult but after some time it would change gradually |
| 40 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | NaN |
| 41 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Agree_Likert | There is need to change because the world is developing hence to move with it |
| 42 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | The keeping of goats helps in protecting the environment because the livelihood is depend on them |
| 43 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | They are a source of income |
| 44 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | So that the government can support us in turn we protect the environment |
| 46 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | If there are people who are teaching you there are new things that you learn |
| 47 | TRALARD_LNM | Agree_Likert | Some things are difficult to look after for instance honey from bee or mushrooms which may be difficult to find |
| 49 | TRALARD_LNM | Agree_Likert | If it is the protected resources we can change |
| 51 | TRALARD_LNM | Agree_Likert | As long as there is help from the government |
| 52 | TRALARD_LNM | Agree_Likert | So that that we can do farming |
| 53 | TRALARD_LNM | Agree_Likert | The project we have can help us improve our livelihoods |
| 56 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | We can change the ways of depending on cutting tree so that we focus on producing honey for our livelihoods |
| 57 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | We have a knowledge that makes us act in the certain way currently and in future we might have new knowledge that would make us act in a different way from the initial one |
| 58 | Ecosystem Conservation_NORTH_WESTERN | Agree_Likert | We are earning from natural resources so that we can develop |
| 59 | Ecosystem Conservation_NORTH_WESTERN | Agree_Likert | The change is easier because it is depent on the people and it can be easily changed |
| 62 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | There are other livelihoods we plan for such as small livestock in the CFMG as well as gardens |
| 63 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | NaN |
| 65 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | If there are other sources of earning a living |
| 66 | Ecosystem Conservation_NORTH_WESTERN | Strongly_Agree_Likert | Through management of the forest and through capacity buildiing |
| 70 | SCRiKA_LS | Strongly_Agree_Likert | There is climate change that forces to change the livelihoods |
| 71 | SCRiKA_LS | Agree_Likert | We only depend on domecticated animals |
| 72 | SCRiKA_LS | Agree_Likert | As long as there is support from somewhere |
| 74 | SCRiKA_LS | Agree_Likert | If there are resources we can change the livelihood |
| 75 | SCRiKA_LS | Agree_Likert | As long as we have no where to depend on for our livelihoods |
| 76 | SCRiKA_LS | Strongly_Agree_Likert | If have have enough water inland than depeneding on the river shores for cultivation purposes then the livelihoods would change |
| 77 | SCRiKA_LS | Agree_Likert | Thing are evolving thus I cannot be stagnant |
| 79 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Agree_Likert | As long as we are helped with altrenative livelihoods |
| 80 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Agree_Likert | We are trying to shift to make gardens for our livelihoods |
| 82 | SCRiKA_LS | Strongly_Agree_Likert | The way things of climate change are it calls for changes |
| 83 | SCRiKA_LS | Agree_Likert | As long as there are different alternative livelihoods |
| 85 | SCRiKA_LS | Agree_Likert | Yes because we are depending of the catle we are looking after thus it is easy to change |
| 86 | SCRiKA_LS | Strongly_Agree_Likert | If there is no profit in one livelihood I can change to another type of livelihood |
| 87 | SCRiKA_LS | Agree_Likert | It is not easy but it is important to change because of climate change where you have to change without taking alternative |
| 88 | SCRiKA_LS | Agree_Likert | Our livelihood depends on farming as our main activities |
| 89 | SCRiKA_LS | Agree_Likert | Changing is difficult but when you get used it is fine |
| 90 | SCRiKA_LS | Strongly_Agree_Likert | Because we are always depending on farming thus a change would lead us to access through that we do not have currently |
| 91 | SCRiKA_LS | Agree_Likert | We are always focused in agriculture thus it is a little bit difficult to change |
| 92 | SCRiKA_LS | Strongly_Agree_Likert | No answer |
| 93 | SCRiKA_LS | Agree_Likert | I would prefer to shift to gardening that other activities |
| 94 | SCRiKA_LS | Agree_Likert | If the change leads to a better life it can be easy |
| 95 | SCRiKA_LS | Agree_Likert | If there are some guidelines to assist in changing the livelihood |
| 97 | SCRiKA_LS | Agree_Likert | I do not know because the livelihood I would change might be worse than the current one |
| 98 | SCRiKA_LS | Agree_Likert | It can be changed with dependency to climate change |
| 100 | SCRiKA_LS | Strongly_Agree_Likert | As long as there are more advantages than disadvantages as well as technology allow it to be done |
| 101 | SCRiKA_LS | Agree_Likert | NaN |
| 102 | SCRiKA_LS | Agree_Likert | But we only depend on the forest and wetlands |
| 104 | SCRiKA_LS | Agree_Likert | If we are changing to the better one |
| 105 | SCRiKA_LS | Strongly_Agree_Likert | Life is hard due to high cost of commodities |
| 110 | SCRiKA_LS | Strongly_Agree_Likert | NaN |
| 111 | SCRiKA_LS | Agree_Likert | There is a problem with the boreholes |
| 113 | SCRiKA_LS | Strongly_Agree_Likert | NaN |
| 114 | SCRiKA_LS | Agree_Likert | It is not easy but it is a good thing |
| 118 | SCRiKA_LS | Agree_Likert | The way we live we adapt any environment like now we have climate change thus we have adapted to challenges |
| 125 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Agree_Likert | If we find someone to assist us we can change |
| 130 | SCReBS_WESTERN | Strongly_Agree_Likert | NaN |
| 132 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Agree_Likert | If we have the knowledge to use thing that have been given to us by the government and live a good life |
| 133 | SCReBS_WESTERN | Agree_Likert | If there is a law that ask us to change we can do it |
| 134 | SCReBS_WESTERN | Agree_Likert | As long as I have help from somewhere |
| 135 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Agree_Likert | It is easier after a long time or process |
| 137 | SCReBS_WESTERN | Strongly_Agree_Likert | As long as we have what to use to change them |
| 138 | SCReBS_WESTERN | Strongly_Agree_Likert | I have limited responsibility thus it is easy to change |
| 140 | SCReBS_WESTERN | Strongly_Agree_Likert | This period is of climate change that calls for different way of doing things |
| 141 | SCReBS_WESTERN | Strongly_Agree_Likert | If am empowered, it is easy |
| 142 | PIN_WESTERN | Strongly_Agree_Likert | If you use the knowledge adquately it is easier |
| 143 | SCReBS_WESTERN | Agree_Likert | As long as there is need for me to change as well as climate change |
| 144 | PIN_WESTERN | Agree_Likert | It is easier as long as there is commitment |
| 146 | PIN_WESTERN | Strongly_Agree_Likert | The person can make a decision through looking back to what he or she has done so as to make corrective measures |
| 147 | PIN_WESTERN | Strongly_Agree_Likert | As long as you are committed |
| 148 | PIN_WESTERN | Strongly_Agree_Likert | It is difficult but as long as you follow what you have been taught you can change |
| 149 | SCReBS_WESTERN | Strongly_Agree_Likert | As long as I have the capacity to do so |
In [502]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CLE_R1.iterrows():
CLE_R1_filter_sentence = []
CLE_R1_sentence = row["Change_Livelihood_Easy_Reasons"]
if pd.isnull(CLE_R1_sentence):
continue
CLE_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CLE_R1_sentence)
CLE_R1_words = nltk.word_tokenize(CLE_R1_sentence_cleaned)
CLE_R1_words = [lemmatizer.lemmatize(w) for w in CLE_R1_words if w.lower() not in stop_words]
CLE_R1_filter_sentence.extend(CLE_R1_words)
print(CLE_R1_filter_sentence)
['SES', 'taken', 'care', 'increase', 'number', 'bring', 'income'] ['SeS', 'depleted', 'thus', 'need', 'change', 'agriculture', 'like', 'goat', 'rearing'] ['Change', 'easier', 'one', 'decides', 'change'] ['thing', 'like', 'climate', 'change', 'affecting', 'u', 'thus', 'thought', 'change'] ['new', 'improvement', 'like', 'cooking', 'stove', 'change', 'way', 'livelihood'] ['destruction', 'SES'] ['need', 'change', 'cutting', 'activity'] ['need', 'change', 'activity', 'destroy', 'environment'] ['changed', 'learning'] ['kept', 'well', 'project', 'like', 'chicken', 'goat', 'would', 'help', 'depend', 'natural', 'resource'] ['fish', 'pond', 'project'] ['also', 'improve', 'livelihood', 'future'] ['long', 'money', 'livelihood'] ['want', 'venture', 'farming', 'reason', 'shifted', 'place', 'located'] ['sub', 'project', 'bee', 'keeping', 'disturbed', 'late', 'burning', 'early', 'burning', 'disturb', 'fire', 'much'] ['need', 'change', 'instance', 'depend', 'water', 'Lulimala', 'river', 'dry', 'get', 'water'] ['natural', 'resource', 'protected', 'accessed'] ['use', 'according', 'accepted', 'norm'] ['Yes', 'normally', 'use', 'natural', 'resource', 'somehow'] ['environment', 'protected', 'business'] ['easier', 'change', 'experience', 'natural', 'resource', 'change', 'accordance', 'climate', 'change'] ['change', 'slowly', 'due', 'low', 'performance', 'group'] ['depend'] ['would', 'difficult', 'time', 'would', 'change', 'gradually'] ['need', 'change', 'world', 'developing', 'hence', 'move'] ['keeping', 'goat', 'help', 'protecting', 'environment', 'livelihood', 'depend'] ['source', 'income'] ['government', 'support', 'u', 'turn', 'protect', 'environment'] ['people', 'teaching', 'new', 'thing', 'learn'] ['thing', 'difficult', 'look', 'instance', 'honey', 'bee', 'mushroom', 'may', 'difficult', 'find'] ['protected', 'resource', 'change'] ['long', 'help', 'government'] ['farming'] ['project', 'help', 'u', 'improve', 'livelihood'] ['change', 'way', 'depending', 'cutting', 'tree', 'focus', 'producing', 'honey', 'livelihood'] ['knowledge', 'make', 'u', 'act', 'certain', 'way', 'currently', 'future', 'might', 'new', 'knowledge', 'would', 'make', 'u', 'act', 'different', 'way', 'initial', 'one'] ['earning', 'natural', 'resource', 'develop'] ['change', 'easier', 'depent', 'people', 'easily', 'changed'] ['livelihood', 'plan', 'small', 'livestock', 'CFMG', 'well', 'garden'] ['source', 'earning', 'living'] ['management', 'forest', 'capacity', 'buildiing'] ['climate', 'change', 'force', 'change', 'livelihood'] ['depend', 'domecticated', 'animal'] ['long', 'support', 'somewhere'] ['resource', 'change', 'livelihood'] ['long', 'depend', 'livelihood'] ['enough', 'water', 'inland', 'depeneding', 'river', 'shore', 'cultivation', 'purpose', 'livelihood', 'would', 'change'] ['Thing', 'evolving', 'thus', 'stagnant'] ['long', 'helped', 'altrenative', 'livelihood'] ['trying', 'shift', 'make', 'garden', 'livelihood'] ['way', 'thing', 'climate', 'change', 'call', 'change'] ['long', 'different', 'alternative', 'livelihood'] ['Yes', 'depending', 'catle', 'looking', 'thus', 'easy', 'change'] ['profit', 'one', 'livelihood', 'change', 'another', 'type', 'livelihood'] ['easy', 'important', 'change', 'climate', 'change', 'change', 'without', 'taking', 'alternative'] ['livelihood', 'depends', 'farming', 'main', 'activity'] ['Changing', 'difficult', 'get', 'used', 'fine'] ['always', 'depending', 'farming', 'thus', 'change', 'would', 'lead', 'u', 'access', 'currently'] ['always', 'focused', 'agriculture', 'thus', 'little', 'bit', 'difficult', 'change'] ['answer'] ['would', 'prefer', 'shift', 'gardening', 'activity'] ['change', 'lead', 'better', 'life', 'easy'] ['guideline', 'assist', 'changing', 'livelihood'] ['know', 'livelihood', 'would', 'change', 'might', 'worse', 'current', 'one'] ['changed', 'dependency', 'climate', 'change'] ['long', 'advantage', 'disadvantage', 'well', 'technology', 'allow', 'done'] ['depend', 'forest', 'wetland'] ['changing', 'better', 'one'] ['Life', 'hard', 'due', 'high', 'cost', 'commodity'] ['problem', 'boreholes'] ['easy', 'good', 'thing'] ['way', 'live', 'adapt', 'environment', 'like', 'climate', 'change', 'thus', 'adapted', 'challenge'] ['find', 'someone', 'assist', 'u', 'change'] ['knowledge', 'use', 'thing', 'given', 'u', 'government', 'live', 'good', 'life'] ['law', 'ask', 'u', 'change'] ['long', 'help', 'somewhere'] ['easier', 'long', 'time', 'process'] ['long', 'use', 'change'] ['limited', 'responsibility', 'thus', 'easy', 'change'] ['period', 'climate', 'change', 'call', 'different', 'way', 'thing'] ['empowered', 'easy'] ['use', 'knowledge', 'adquately', 'easier'] ['long', 'need', 'change', 'well', 'climate', 'change'] ['easier', 'long', 'commitment'] ['person', 'make', 'decision', 'looking', 'back', 'done', 'make', 'corrective', 'measure'] ['long', 'committed'] ['difficult', 'long', 'follow', 'taught', 'change'] ['long', 'capacity']
In [503]:
CLE_R1["Change_Livelihood_Easy_Reasons"] = CLE_R1["Change_Livelihood_Easy_Reasons"].fillna("")
CLE_R1["Change_Livelihood_Easy_Reasons"] = CLE_R1["Change_Livelihood_Easy_Reasons"].astype(str)
CLE_R1_Text = " ".join(CLE_R1["Change_Livelihood_Easy_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CLE_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Change Livelihood Easy", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
In [504]:
agreement_levels = ["Disagree_Likert", "Strongly_Disagree_Likert"]
CLE_R2 = df2[df2["Change_Livelihood_Easy"].isin(agreement_levels)]
CLE_R3 = CLE_R2.drop(CLE_R2.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CLE_R3grouped = CLE_R3.groupby('Name_Main_Project')['Change_Livelihood_Easy']
CLE_R3
Out[504]:
| Name_Main_Project | Change_Livelihood_Easy | Change_Livelihood_Easy_Reasons | |
|---|---|---|---|
| 3 | TRALARD_LNM | Strongly_Disagree_Likert | Because my livelihood would improve |
| 10 | TRALARD_LNM | Strongly_Disagree_Likert | Because everything we use comes from natural resources like tree for building, animals for proteins |
| 16 | TRALARD_LNM | Strongly_Disagree_Likert | It is a source of income |
| 18 | TRALARD_LNM | Strongly_Disagree_Likert | They can assist in having adequate water if trees are not cut as well as future general to see the natural resources |
| 19 | TRALARD_LNM | Disagree_Likert | The world is becoming mordenised thus we need to adapt to the current status |
| 20 | TRALARD_LNM | Disagree_Likert | Thats where some of our income comes from |
| 23 | EbA_CENTRAL_MUCHINGA_LUAPULA | Disagree_Likert | It is because we depend on natural resources thus changing is not easy |
| 24 | TRALARD_LNM | Disagree_Likert | I do not use natural resources |
| 28 | TRALARD_LNM | Strongly_Disagree_Likert | We depend on agriculture and not the natural resource like forest |
| 31 | TRALARD_LNM | Disagree_Likert | We mostly use goats and pigs in our livelihoods |
| 36 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | There are the livelihoods we engage in such as keeping goats and fish farming |
| 37 | TRALARD_LNM | Disagree_Likert | I have some other livelihood like keeping goat chickens gardening and hiring of wedding dresses |
| 45 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | I only use domesticated animals |
| 50 | TRALARD_LNM | Disagree_Likert | We do not use them |
| 54 | TRALARD_LNM | Disagree_Likert | There are things of just learning more in what we are already doing |
| 55 | TRALARD_LNM | Disagree_Likert | NaN |
| 64 | Ecosystem Conservation_NORTH_WESTERN | Disagree_Likert | We do not have money for protection of the environment and we have not yet had money from the carbon trade and we have been waiting for it for long time now |
| 68 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | NaN |
| 69 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | NaN |
| 78 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | Because am used to it |
| 81 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | When changing to another lifestyle it means that you are starting a new life |
| 96 | SCRiKA_LS | Disagree_Likert | It is but currently there is drought thus difficult to change |
| 103 | SCRiKA_LS | Strongly_Disagree_Likert | The natural resources were given to us by God and we depend on roots from trees for medicine and other fruits |
| 106 | SCRiKA_LS | Disagree_Likert | We have only one activity that we do here such as farming thus changing is difficult |
| 107 | SCRiKA_LS | Disagree_Likert | Our livelihood is just based on farming |
| 108 | SCRiKA_LS | Disagree_Likert | We always use products from the forest |
| 109 | SCRiKA_LS | Disagree_Likert | Our livelihood is difficult at the moment because of climate change |
| 112 | SCRiKA_LS | Disagree_Likert | There is no problem thus we cannot change |
| 115 | SCRiKA_LS | Strongly_Disagree_Likert | The person can not change unless the person is shown what to do |
| 117 | SCRiKA_LS | Strongly_Disagree_Likert | We can not manage to live without depending on the forest like our craw we use the trees |
| 119 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | NaN |
| 120 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | NaN |
| 121 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Disagree_Likert | It is only the starting point that is difficult to change because in needs finances |
| 122 | SCReBS_WESTERN | Disagree_Likert | Because i do not have the livelihood am shifting to |
| 124 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | We have our livelihoods |
| 126 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | Am not sure of the future thus i cannaot change |
| 127 | SCReBS_WESTERN | Strongly_Disagree_Likert | It is a challenge |
| 128 | SCReBS_WESTERN | Strongly_Disagree_Likert | The firewood we depend on is from trees thus it is a challenge to change. Agriculture means cutting down trees and building houses depends on the trees. Also, the piggery project that is been done does not have market thus the project is not effective |
| 129 | SCReBS_WESTERN | Strongly_Disagree_Likert | It is difficult because we do not have money and the pigs we are keeping it is a challenge looking after them and most of them die |
| 131 | SCReBS_WESTERN | Strongly_Disagree_Likert | The person leading a better life he cannot change |
| 136 | SCRALA_SOUTHERN_WESTERN_NORTHEN | Strongly_Disagree_Likert | Am at an old age thus i cannot change my livelihood |
| 139 | SCReBS_WESTERN | Disagree_Likert | I just have to continue trying |
In [505]:
lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CLE_R3.iterrows():
CLE_R3_filter_sentence = []
CLE_R3_sentence = row["Change_Livelihood_Easy_Reasons"]
if pd.isnull(CLE_R3_sentence):
continue
CLE_R3_sentence_cleaned = re.sub(r'[^\w\s]','',CLE_R3_sentence)
CLE_R3_words = nltk.word_tokenize(CLE_R3_sentence_cleaned)
CLE_R3_words = [lemmatizer.lemmatize(w) for w in CLE_R3_words if w.lower() not in stop_words]
CLE_R3_filter_sentence.extend(CLE_R3_words)
print(CLE_R3_filter_sentence)
['livelihood', 'would', 'improve'] ['everything', 'use', 'come', 'natural', 'resource', 'like', 'tree', 'building', 'animal', 'protein'] ['source', 'income'] ['assist', 'adequate', 'water', 'tree', 'cut', 'well', 'future', 'general', 'see', 'natural', 'resource'] ['world', 'becoming', 'mordenised', 'thus', 'need', 'adapt', 'current', 'status'] ['Thats', 'income', 'come'] ['depend', 'natural', 'resource', 'thus', 'changing', 'easy'] ['use', 'natural', 'resource'] ['depend', 'agriculture', 'natural', 'resource', 'like', 'forest'] ['mostly', 'use', 'goat', 'pig', 'livelihood'] ['livelihood', 'engage', 'keeping', 'goat', 'fish', 'farming'] ['livelihood', 'like', 'keeping', 'goat', 'chicken', 'gardening', 'hiring', 'wedding', 'dress'] ['use', 'domesticated', 'animal'] ['use'] ['thing', 'learning', 'already'] ['money', 'protection', 'environment', 'yet', 'money', 'carbon', 'trade', 'waiting', 'long', 'time'] ['used'] ['changing', 'another', 'lifestyle', 'mean', 'starting', 'new', 'life'] ['currently', 'drought', 'thus', 'difficult', 'change'] ['natural', 'resource', 'given', 'u', 'God', 'depend', 'root', 'tree', 'medicine', 'fruit'] ['one', 'activity', 'farming', 'thus', 'changing', 'difficult'] ['livelihood', 'based', 'farming'] ['always', 'use', 'product', 'forest'] ['livelihood', 'difficult', 'moment', 'climate', 'change'] ['problem', 'thus', 'change'] ['person', 'change', 'unless', 'person', 'shown'] ['manage', 'live', 'without', 'depending', 'forest', 'like', 'craw', 'use', 'tree'] ['starting', 'point', 'difficult', 'change', 'need', 'finance'] ['livelihood', 'shifting'] ['livelihood'] ['sure', 'future', 'thus', 'cannaot', 'change'] ['challenge'] ['firewood', 'depend', 'tree', 'thus', 'challenge', 'change', 'Agriculture', 'mean', 'cutting', 'tree', 'building', 'house', 'depends', 'tree', 'Also', 'piggery', 'project', 'done', 'market', 'thus', 'project', 'effective'] ['difficult', 'money', 'pig', 'keeping', 'challenge', 'looking', 'die'] ['person', 'leading', 'better', 'life', 'change'] ['old', 'age', 'thus', 'change', 'livelihood'] ['continue', 'trying']
In [506]:
CLE_R3["Change_Livelihood_Easy_Reasons"] = CLE_R3["Change_Livelihood_Easy_Reasons"].fillna("")
CLE_R3["Change_Livelihood_Easy_Reasons"] = CLE_R3["Change_Livelihood_Easy_Reasons"].astype(str)
CLE_R3_Text = " ".join(CLE_R1["Change_Livelihood_Easy_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CLE_R3_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Change Livelihood Easy", loc="left", fontsize=20, pad=20)
plt.axis("off")
plt.show()
11.8 Main Project and Ecosystem Service Reduction¶
In [507]:
ESR_grouped = df2.groupby('Name_Main_Project')['Ecosystem_Services_Reduced'].value_counts(dropna=False)
In [508]:
ESR_grouped1 = pd.DataFrame(ESR_grouped)
In [509]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = ESR_grouped1, x="count", y="Name_Main_Project", hue="Ecosystem_Services_Reduced", hue_order=hue_order, legend=True)
ax.set_title("Figure 20: Number of Responses in each of the main Project on if Ecosystem services have Reduced", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
11.9 Main project and Deforestation Increase¶
In [510]:
DI_grouped = df2.groupby('Name_Main_Project')['Deforestaion_Increased'].value_counts(dropna=False)
In [511]:
DI_grouped1 = pd.DataFrame(DI_grouped)
In [512]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = DI_grouped1, x="count", y="Name_Main_Project", hue="Deforestaion_Increased", hue_order=hue_order, legend=True)
ax.set_title("Figure 21: Number of Responses in each of the main Project on if Deforestation has Increased", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
11.10 Main project and Protected Areas as a Hinderance¶
In [513]:
PAHL_grouped = df2.groupby('Name_Main_Project')['Protected_Areas_Hinderarnce_Livelihood'].value_counts(dropna=False)
In [514]:
PAHL_grouped1 = pd.DataFrame(PAHL_grouped)
In [515]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = PAHL_grouped1, x="count", y="Name_Main_Project", hue="Protected_Areas_Hinderarnce_Livelihood", hue_order=hue_order, legend=True)
ax.set_title("Figure 22: Number of Responses in each of the main Project on if Protected Areas are a Hinderance to Livelihood", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
11.11 Main Project and New Livelihood Projects¶
In [516]:
NLP_grouped = df2.groupby('Name_Main_Project')['New_Livelihood_Projects'].value_counts(dropna=False)
In [517]:
NLP_grouped1 = pd.DataFrame(NLP_grouped)
In [518]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = NLP_grouped1, x="count", y="Name_Main_Project", hue="New_Livelihood_Projects", hue_order=hue_order, legend=True)
ax.set_title("Figure 23: Number of Responses in each of the main Project on if some Livelihood Subprojects not Implemented", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
11.12 Main Project and Sustainability of Subprojects¶
In [519]:
SSC_grouped = df2.groupby('Name_Main_Project')['Subprojects_Sustainability_Contribution'].value_counts(dropna=False)
In [520]:
SSC_grouped1 = pd.DataFrame(SSC_grouped)
In [521]:
plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = SSC_grouped1, x="count", y="Name_Main_Project", hue="Subprojects_Sustainability_Contribution", hue_order=hue_order, legend=True)
ax.set_title("Figure 24: Number of Responses in each of the main Project on if Subprojects Contribute to Sustainability", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
12. Converting the Notebook¶
In [522]:
with open('02_Landscape_Transformation_Livelihood.ipynb', 'r') as f:
notebook = nbformat.read(f, as_version=4)
# Initialize the HTML Exporter
html_exporter = HTMLExporter()
(body, resources) = html_exporter.from_notebook_node(notebook)
# Save the HTML output
with open('02_Landscape_Transformation_Livelihood.html', 'w') as f:
f.write(body)
print("Conversion to HTML completed!")
Conversion to HTML completed!